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18 pages, 14274 KiB  
Article
The Evolution of Powell Basin (Antarctica)
by Alberto Santamaría Barragán, Manuel Catalán and Yasmina M. Martos
Remote Sens. 2024, 16(21), 4053; https://doi.org/10.3390/rs16214053 - 31 Oct 2024
Viewed by 492
Abstract
Powell Basin is an ocean basin formed as a result of the Scotia Sea evolution. The existing tectonic models propose a variety of starting and ending ages for the spreading of the basin based on seafloor magnetic anomalies. Here, we use recent magnetic [...] Read more.
Powell Basin is an ocean basin formed as a result of the Scotia Sea evolution. The existing tectonic models propose a variety of starting and ending ages for the spreading of the basin based on seafloor magnetic anomalies. Here, we use recent magnetic field data obtained from eight magnetic profiles in Powell Basin to provide insights into the oceanic spreading evolution. The differences found between the number of anomalies on both sides of the axis and the asymmetry in the spreading rates suggest different opening models for different parts of the basin. We propose a spreading model starting in the late Eocene (38.08 Ma) and ending in the early Miocene (21.8 Ma) for the northern part of Powell Basin. For the southern part, the opening started in the late Eocene (38.08 Ma) and ended in the middle Paleogene (25.2 Ma). The magnetic data have been combined with gravity and sediment thickness data to better constrain the age models. The gravity and sediment thickness information allow us to more accurately locate the position of the extinct spreading axis. Geothermal heat flow measurements are used to understand the relationship between the low amplitudes of the magnetic anomalies and the heat beneath them. Our proposed oceanic spreading models suggest that the initial incursions of the Pacific mantle outflow into the Powell Basin occurred in the Oligocene, and the initial incursions of oceanic currents from the Weddell Sea occurred in the Eocene. Full article
(This article belongs to the Special Issue Antarctic Remote Sensing Applications (Second Edition))
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Figure 1
<p>Scotia Sea and Powell Basin setting. (<b>A</b>) Bathymetry and geological setting of the study area (modified from Global Multi-Resolution Topography data grid GMRT [<a href="#B6-remotesensing-16-04053" class="html-bibr">6</a>]). The red dot indicates the geographical position of the magnetic observatory in Livingston Island. (<b>B</b>) Bathymetry map of the Powell Basin with magnetic profiles obtained during the ElGeoPoweR expedition. Morphological and oceanographic features: AP, Antarctic Peninsula; DB, Dove Basin; JB, Jane Basin; OB, Ona Basin; PB, Powell Basin; Pib, Pirie Basin; PrB, Protector Basin; SB, Scan Basin; SFZ, Shackleton Fracture Zone; SOM, South Orkney Microcontinent; 1, Active fracture zone; 2, Transcurrent fault; 3, Active subduction zone; 4, Active spreading center; and 5, Active extensional zone. Water masses: CDW, Circumpolar Deep Water; WSDW, Weddell Sea Deep Water.</p>
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<p>Magnetic signature of the Powell Basin. (<b>A</b>) Magnetic anomaly map of the Powell Basin. Blue lines represent the magnetic profiles collected during the ElGeoPoweR expedition. (<b>B</b>) Magnetic anomaly map of the Powell Basin high pass filtered 50 km and the magnetic anomaly wiggles identified in each profile. PMA, Pacific Margin Anomaly; SOM, South Orkney Microcontinent; AP, Antarctic Peninsula. 1. Oceanic crust boundary. 2. Intruded and thinned continental crust boundary. 3. Extended and thinned continental crust boundary (boundaries proposed by [<a href="#B33-remotesensing-16-04053" class="html-bibr">33</a>]).</p>
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<p>Free-air gravity map of the Powell Basin. The white dash line defines the geological boundaries of the Powell basin. The black dashed line shows a Y-shaped anomaly high in the center of the basin. The blue dashed line represents the position of the extinct spreading axis. The black lines indicate the position of the magnetic profiles. PMA, Pacific Margin Anomaly; SOM, South Orkney Microcontinent; and AP, Antarctic Peninsula.</p>
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<p>Oceanic spreading model based on magnetic anomalies for the northern part of the Powell Basin. (<b>A</b>) Magnetic profiles of Powell Basin (see <a href="#remotesensing-16-04053-f002" class="html-fig">Figure 2</a>A for location). The light blue colors represent the sections of the magnetic profiles where the anomalies are situated outside the oceanic crust. This part of the profiles has not been used for modeling. The dashed vertical lines indicate the limits of the oceanic crust. (<b>B</b>) Synthetic spreading model for 38.08–21.8 Ma. A0 corresponds to the position of the extinct spreading axis. The identified anomalies are labeled as A. (<b>C</b>) Representation of the magnetic layer of profile L3 including the basement topography and a thickness of 0.5 km. Assigned Chrons are indicated here. (<b>D</b>) Spreading rates.</p>
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<p>Oceanic spreading model based on magnetic anomalies in the southern part of the Powell Basin. (<b>A</b>) Magnetic profiles of the Powell Basin (see <a href="#remotesensing-16-04053-f002" class="html-fig">Figure 2</a>A for location). The light blue colors represent the sections of the magnetic profiles where the anomalies are situated outside the oceanic crust. This part of the profile has not been used for modeling. The dashed vertical lines indicate the limits of the oceanic crust. (<b>B</b>) Synthetic spreading model for 38.08–25.2 Ma. A0 corresponds to the position of the extinct spreading axis. The identified anomalies are labeled as A. (<b>C</b>) Representation of the magnetic layer of profile L3 including the basement topography and a thickness of 0.5 km. Assigned Chrons are indicated here. (<b>D</b>) Spreading rates.</p>
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<p>Comparison of EL2002 and our models. (<b>A</b>) Comparison of the positions of the magnetic anomalies in our profile L3 located in the northern area with the profile from EL2002. (<b>B</b>) Comparison of the positions of the magnetic anomalies obtained in the synthetic oceanic spreading models. The red dashed line represents the anomalies from the EL2002 synthetic model and their southern profile. The black line is our model. P1–P6 are the magnetic anomalies set in the profile from EL2002 and the synthetic model. A1–A5 are the magnetic anomalies set in our synthetic model of the Powell Basin (<a href="#remotesensing-16-04053-f004" class="html-fig">Figure 4</a>). The blue dashed vertical lines mark the boundaries between different types of crust (<a href="#remotesensing-16-04053-f002" class="html-fig">Figure 2</a>A). The gray dashed vertical line indicates the position of the extinct spreading axis for both models.</p>
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16 pages, 12826 KiB  
Article
Seasonal and Interannual Variations in Sea Ice Thickness in the Weddell Sea, Antarctica (2019–2022) Using ICESat-2
by Mansi Joshi, Alberto M. Mestas-Nuñez, Stephen F. Ackley, Stefanie Arndt, Grant J. Macdonald and Christian Haas
Remote Sens. 2024, 16(20), 3909; https://doi.org/10.3390/rs16203909 - 21 Oct 2024
Viewed by 806
Abstract
The sea ice extent in the Weddell Sea exhibited a positive trend from the start of satellite observations in 1978 until 2016 but has shown a decreasing trend since then. This study analyzes seasonal and interannual variations in sea ice thickness using ICESat-2 [...] Read more.
The sea ice extent in the Weddell Sea exhibited a positive trend from the start of satellite observations in 1978 until 2016 but has shown a decreasing trend since then. This study analyzes seasonal and interannual variations in sea ice thickness using ICESat-2 laser altimetry data over the Weddell Sea from 2019 to 2022. Sea ice thickness was calculated from ICESat-2’s ATL10 freeboard product using the Improved Buoyancy Equation. Seasonal variability in ice thickness, characterized by an increase from February to September, is more pronounced in the eastern Weddell sector, while interannual variability is more evident in the western Weddell sector. The results were compared with field data obtained between 2019 and 2022, showing a general agreement in ice thickness distributions around predominantly level ice. A decreasing trend in sea ice thickness was observed when compared to measurements from 2003 to 2017. Notably, the spring of 2021 and summer of 2022 saw significant decreases in Sea Ice Extent (SIE). Although the overall mean sea ice thickness remained unchanged, the northwestern Weddell region experienced a noticeable decrease in ice thickness. Full article
(This article belongs to the Special Issue Monitoring Sea Ice Loss with Remote Sensing Techniques)
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<p>(<b>a</b>) Sea ice concentration from NSIDC for Antarctica in January 2022 with solid dark blue depicting ice concentrations smaller than 15% and white indicating 100% ice. The study area in the Weddell Sea is indicated by the yellow polygon. (<b>b</b>) Expanded study area map showing the location of ICESat-2 tracks for September 2022. Also shown are the 45°W meridian, which divides the study area into eastern and western sectors, and the 68°S parallel, which further divides the western sector into northwestern and southwestern regions, for the purpose of this study. The solid blue dots indicate the approximate locations of the field observations used in this study, which were obtained from 2019 to 2022.</p>
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<p>(<b>a</b>–<b>p</b>) Multi-panel maps of the study area showing ICESat-2 total freeboard tracks from 2019 to 2022 for different seasons. The bottom two panels show: (<b>q</b>) the modal values of freeboard in meters for the western Weddell (solid color circles) with second modal values shown by the color crosses, 2019 (red), 2020 (black), 2021 (green), and 2022 (blue); and (<b>r</b>) the same as (<b>q</b>) but for the eastern Weddell.</p>
Full article ">Figure 2 Cont.
<p>(<b>a</b>–<b>p</b>) Multi-panel maps of the study area showing ICESat-2 total freeboard tracks from 2019 to 2022 for different seasons. The bottom two panels show: (<b>q</b>) the modal values of freeboard in meters for the western Weddell (solid color circles) with second modal values shown by the color crosses, 2019 (red), 2020 (black), 2021 (green), and 2022 (blue); and (<b>r</b>) the same as (<b>q</b>) but for the eastern Weddell.</p>
Full article ">Figure 2 Cont.
<p>(<b>a</b>–<b>p</b>) Multi-panel maps of the study area showing ICESat-2 total freeboard tracks from 2019 to 2022 for different seasons. The bottom two panels show: (<b>q</b>) the modal values of freeboard in meters for the western Weddell (solid color circles) with second modal values shown by the color crosses, 2019 (red), 2020 (black), 2021 (green), and 2022 (blue); and (<b>r</b>) the same as (<b>q</b>) but for the eastern Weddell.</p>
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<p>Thickness estimates of the western Weddell (<b>left</b>—<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) and eastern Weddell (<b>right</b>—<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) regions from 2019 to 2022. Mean, mode, and Standard Deviation (SD) in meters. Bimodal mode values in the western Weddell are depicted in parenthesis.</p>
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<p>Location of ICESat-2 tracks in red with field data in blue. Field data from 2019 and 2021 are point measurements. The data from 2022 are thickness measurements on board Endurance 22 ship EM data.</p>
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<p>Mean thickness from (<b>a</b>) northwestern and (<b>b</b>) southwestern Weddell Sea.</p>
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<p>ERA5 monthly averaged 2 m air temperature in Kelvin for the (<b>a</b>) western and (<b>b</b>) eastern Weddell, 2019–2022.</p>
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<p>Comparison of ICESat and IceBridge mean thickness results in blue from [<a href="#B7-remotesensing-16-03909" class="html-bibr">7</a>] using the same method, with ICESat-2 results from this study in orange.</p>
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16 pages, 5328 KiB  
Article
Application of HY-2B Satellite Data to Retrieve Snow Depth on Antarctic Sea Ice
by Qing Ji, Nana Liu, Mengqin Yu, Zhiming Zhang, Zehui Xiao and Xiaoping Pang
Remote Sens. 2024, 16(17), 3253; https://doi.org/10.3390/rs16173253 - 2 Sep 2024
Viewed by 588
Abstract
Sea ice and its surface snow are crucial components of the energy cycle and mass balance between the atmosphere and ocean, serving as sensitive indicators of climate change. Observing and understanding changes in snow depth on Antarctic sea ice are essential for sea [...] Read more.
Sea ice and its surface snow are crucial components of the energy cycle and mass balance between the atmosphere and ocean, serving as sensitive indicators of climate change. Observing and understanding changes in snow depth on Antarctic sea ice are essential for sea ice research and global climate change studies. This study explores the feasibility of retrieving snow depth on Antarctic sea ice using data from the Chinese marine satellite HY-2B. Using generic retrieval algorithms, snow depth on Antarctic sea ice was retrieved from HY-2B Scanning Microwave Radiometer (SMR) data, and compared with existing snow depth products derived from other microwave radiometer data. A comparison against ship-based snow depth measurements from the Chinese 35th Antarctic Scientific Expedition shows that snow depth derived from HY-2B SMR data using the Comiso03 retrieval algorithm exhibits the lowest RMSD, with a deviation of −1.9 cm compared to the Markus98 and Shen22 models. The snow depth derived using the Comiso03 model from HY-2B SMR shows agreement with the GCOM-W1 AMSR-2 snow depth product released by the National Snow and Ice Data Center (NSIDC). Differences between the two primarily occur during the sea ice ablation and in the Bellingshausen Sea, Amundsen Sea, and the southern Pacific Ocean. In 2019, the monthly average snow depth on Antarctic sea ice reached its maximum in January (36.2 cm) and decreased to its minimum in May (15.3 cm). Thicker snow cover was observed in the Weddell Sea, Ross Sea, and Bellingshausen and Amundsen seas, primarily due to the presence of multi-year ice, while thinner snow cover was found in the southern Indian Ocean and the southern Pacific Ocean. The derived snow depth product from HY-2B SMR data demonstrates high accuracy in retrieving snow depth on Antarctic sea ice, highlighting its potential as a reliable alternative for snow depth measurements. This product significantly contributes to observing and understanding changes in snow depth on Antarctic sea ice and its relationship with climate change. Full article
(This article belongs to the Section Ocean Remote Sensing)
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<p>(<b>a</b>) Five sectors in the Southern Ocean: the blue line represents the R/V XueLong routes during the 35th Chinese National Antarctic Research Expedition (CHINARE-35), and the red dots indicate the measurement points of snow depth on sea ice conducted by the ship, (<b>b</b>,<b>c</b>) are the brightness temperature measured by HY-2B SMR at 18.7 GHz on 1 January and 1 July, 2019, respectively, and (<b>d</b>,<b>e</b>) are the GCOM-W1 AMSR-2 snow depth products released by the NSIDC on 1 January and 1 July, 2019, respectively.</p>
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<p>(<b>a</b>) A measured camera fixed on the R/V XueLong used to record sea ice and snow situation during the 35th Chinese National Antarctic Research Expedition and (<b>b</b>) snow depth estimation involving photogrammetric images. The long red line in (<b>b</b>) denotes the reference ball diameter and the short red lines denote snow depth on sea ice.</p>
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<p>Comparison of fixed (traditional method) and dynamic (this study) tie points calculated from the HY-2B SMR measured brightness temperature at 19 and 37 GHz for open sea water in 2019.</p>
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<p>Spatial distribution of the retrieved HY-2B snow depth based on the Markus98 model (<b>a</b>), Comiso03 model (<b>b</b>), and Shen22 model (<b>c</b>).</p>
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<p>Daily spatial average differences of HY-2B SMR snow depth and GCOM-W1 AMSR2 snow depth on Antarctic sea ice in 2019.</p>
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<p>Spatial distribution of HY-2B SMR derived monthly snow depth in 2019.</p>
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<p>Time-series of snow depth on Antarctic sea ice and its various sea areas derived from HY-2B SMR data in 2019. The red solid line is the daily HY-2B SMR snow depth and the blue dashed line is the monthly average HY-2B SMR snow depth.</p>
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15 pages, 815 KiB  
Article
Diet and Feeding Behavior of the South Polar Skuas Stercorarius maccormicki in the Haswell Islands, East Antarctica
by Sergey Golubev
Birds 2024, 5(2), 240-254; https://doi.org/10.3390/birds5020016 - 31 May 2024
Viewed by 1132
Abstract
The diet and feeding behavior of South Polar Skuas (Stercorarius maccormicki) are well studied within the species’ breeding range but are poorly understood on the Haswell Islands. The aim of this study was to determine how South Polar Skuas use available [...] Read more.
The diet and feeding behavior of South Polar Skuas (Stercorarius maccormicki) are well studied within the species’ breeding range but are poorly understood on the Haswell Islands. The aim of this study was to determine how South Polar Skuas use available resources during the pre-breeding and breeding periods at the Haswell Archipelago (66°31′ S, 93°01′ E, Davis Sea, Southern Ocean) under conditions of prolonged human activity. I studied pellets, spontaneous regurgitation, and stomach contents of feathered birds to study the diet of skuas and used direct observations of their feeding behavior. South Polar Skuas at the Haswell Islands fed primarily on the Emperor Penguin (Aptenodytes forsteri) colony and on terrestrial resources in the Adélie Penguin (Pygoscelis adeliae) and fulmarine petrel colonies. The dominant prey of skuas were breeding Antarctic penguins. Emperor Penguins and Adélie Penguins make up the bulk of the skuas’ diet in the pre-breeding and breeding periods. Surface feeding at sea was observed in the post-breeding period. In recent decades, kitchen waste supported the skua population. Scavenging (placenta and feces of Weddell seals [Leptonychotes weddellii], frozen eggs, chicks and adults of breeding bird species, kitchen refuse) is the dominant strategy for obtaining food. Adélie Penguin eggs and chicks were the main food items of the South Polar Skuas in the Haswell Archipelago. Skua predation could potentially influence the breeding success of Adélie Penguins and fulmarine petrels, but the extent of the impact is unknown. The impact of the South Polar Skua on Emperor Penguins is negligible because skuas feed mainly on frozen chicks and eggs of the species. Full article
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<p>Study area (the red circle in the inset in the upper left corner indicates the location of the Mirny Station). Note: the yellow spot is a colony of Emperor Penguins (<span class="html-italic">Aptenodytes forsteri</span>); blue circles are colonies of Adélie Penguins (<span class="html-italic">Pygoscelis adelia</span>); green circles are the breeding grounds of South Polar Skuas (<span class="html-italic">Stercorarius maccormicki</span>); pink circles are the breeding grounds of Brown Skuas (<span class="html-italic">Stercorarius antarcticus</span>).</p>
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<p>South Polar Skuas (<span class="html-italic">Stercorarius maccormicki</span>) feeding on Weddell seal (<span class="html-italic">Leptonychotes weddellii</span>) feces. Haswell Archipelago. 11 December 2012.</p>
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15 pages, 5518 KiB  
Article
Ross–Weddell Dipole Critical for Antarctic Sea Ice Predictability in MPI–ESM–HR
by Davide Zanchettin, Kameswarrao Modali, Wolfgang A. Müller and Angelo Rubino
Atmosphere 2024, 15(3), 295; https://doi.org/10.3390/atmos15030295 - 28 Feb 2024
Viewed by 1151
Abstract
We use hindcasts from a state-of-the-art decadal climate prediction system initialized between 1979 and 2017 to explore the predictability of the Antarctic dipole—that is, the seesaw between sea ice cover in the Weddell and Ross Seas, and discuss its implications for Antarctic sea [...] Read more.
We use hindcasts from a state-of-the-art decadal climate prediction system initialized between 1979 and 2017 to explore the predictability of the Antarctic dipole—that is, the seesaw between sea ice cover in the Weddell and Ross Seas, and discuss its implications for Antarctic sea ice predictability. Our results indicate low forecast skills for the Antarctic dipole in the first hindcast year, with a strong relaxation of March values toward the climatology contrasting with an overestimation of anomalies in September, which we interpret as being linked to a predominance of local drift processes over initialized large-scale dynamics. Forecast skills for the Antarctic dipole and total Antarctic sea ice extent are uncorrelated. Limited predictability of the Antarctic dipole is also found under preconditioning around strong warm and strong cold events of the El Niño-Southern Oscillation. Initialization timing and model drift are reported as potential explanations for the poor predictive skills identified. Full article
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<p>Antarctic sea ice predictions in MPI–ESM–HR. (<b>a</b>,<b>b</b>) Monthly-mean Antarctic total sea ice extent (<b>a</b>) and Antarctic dipole or ADP (<b>b</b>) anomalies, defined as deviations from the monthly climatology, in the assimilation run (black: raw data; green: detrended data) and in the first (blue) and second (orange) hindcast years initialized from 1979 to 2017; (<b>c</b>,<b>d</b>) root mean squared errors (<b>c</b>) and persistence index (<b>d</b>) for first year March and September predictions (detrended data), with indication of years clustered according to positive (pale green) and negative (brown) phases of the ADP. (<b>e</b>–<b>j</b>) sea ice concentration trends (top, values per decade) and anomalies associated with the clusters for negative (mid) and positive (bottom) phases of the ADP for March (<b>e</b>,<b>g</b>,<b>i</b>) and September (<b>f</b>,<b>h</b>,<b>j</b>). Only anomalies significant at <span class="html-italic">p</span> = 0.1 (trend) and <span class="html-italic">p</span> = 0.25 (cluster) are plotted. The thick (thin) lines are the climatological sea ice edge at the 0 and 0.15 sea ice concentration levels.</p>
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<p>Relationship between total Antarctic total sea ice extent and Antarctic dipole (ADP) in MPI–ESM–HR. (<b>a</b>) Correlation coefficients between detrended 1979–2018 annual time series for individual months (x-axis reference is for ADP), where total Antarctic sea ice extent data are from the same as month as for ADP (red), the preceding month (orange) or the following month (blue). Squares indicate statistically significant correlations (<span class="html-italic">p</span> &lt; 0.05); (<b>b</b>–<b>e</b>) regressions of sea ice concentration on the ADP (10<sup>12</sup> km<sup>2</sup>) for January, with ADP leading by one month (<b>b</b>), January at lag-0 (<b>c</b>), January, with ADP lagging by one month (<b>d</b>), February at lag-0 (<b>e</b>). Only local regression significant at <span class="html-italic">p</span> = 0.1 are shown. The thick (thin) lines are the climatological sea ice edge at the 0 and 0.15 sea ice concentration levels.</p>
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<p>Predictions of key components of Antarctic sea ice variability in MPI–ESM–HR. (<b>a</b>–<b>d</b>) Cluster analysis of monthly-mean March (left) and September (right) shows anomalies of the Antarctic dipole (ADP) (<b>a</b>), Weddell (<b>b</b>) and Ross (<b>c</b>) sea ice area and Nino3.4 (<b>d</b>) clustered around strong negative (green) and strong positive (red) phases of the ADP, in assimilation and first year hindcasts. The numbers at the bottom are <span class="html-italic">p</span> values for a rank sum test that compares the two clusters; (<b>e</b>–<b>j</b>) sea ice concentration trends (top, values per decade) and anomalies associated with the clusters for negative (mid) and positive (bottom) phases of the ADP for March (<b>e</b>,<b>g</b>,<b>i</b>) and September (<b>f</b>,<b>h</b>,<b>j</b>) in the first year hindcasts. Only anomalies significant at <span class="html-italic">p</span> = 0.1 (trend) and <span class="html-italic">p</span> = 0.25 (cluster) are plotted. The thick (thin) lines are the climatological sea ice edge at the 0 and 0.15 sea ice concentration levels.</p>
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<p>Evolution of Antarctic sea ice, sea surface temperature and atmospheric circulation toward strong positive phases of the Antarctic dipole, or ADP, in March, simulated in MPI–ESM–HR. (<b>a</b>–<b>e</b>) Anomalies for the assimilation run; (<b>f</b>–<b>j</b>) anomalies for the hindcasts. Only statistically significant anomalies at <span class="html-italic">p</span> = 0.25 are shown, except for atmospheric circulation changes (continuous contour: significant; dashed contour: non-significant). Data are linearly detrended. Sea surface temperature anomalies are only shown until 60° S. Atmospheric circulation changes, only available for the assimilation run, are diagnosed via geopotential height at 300 hPa (blue: positive; green: negative).</p>
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<p>Evolution of the drift in Antarctic sea ice concentration in MPI–ESM–HR. Hindcast average error in sea ice concentration (units are grid area fraction) during the first five forecast months, including November (<b>a</b>), December (<b>b</b>), January (<b>c</b>), February (<b>d</b>) and March (<b>e</b>). The thick (thin) lines are the climatological sea ice edge at the 0 and 0.15 sea ice concentration levels.</p>
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<p>ENSO as a precursor of Antarctic sea ice variability in MPI–ESM–HR. Evolution of Antarctic dipole or ADP (<b>a</b>), Weddell (<b>b</b>) and Ross (<b>c</b>) sea ice cover and Nino3.4 index (<b>d</b>) anomalies for prominent warm (yellow: assimilation; brown: hindcasts) and cold (blue: assimilation; green: hindcasts) ENSO events. Data are deseasonalized and smoothed with a 3-month running average. Black (grey; large: hindcast mean; small: individual hindcasts) squares indicate when the difference between anomalies under warm and cold ENSO is statistically significant (<span class="html-italic">p</span> = 0.1). Hindcasts are initialized in November of Year 0.</p>
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18 pages, 6201 KiB  
Article
Distribution Characteristics and Influencing Factors of Sea Ice Leads in the Weddell Sea, Antarctica
by Yueyun Wang, Qing Ji, Xiaoping Pang, Meng Qu, Mingxing Cha, Fanyi Zhang, Zhongnan Yan and Bin He
Remote Sens. 2023, 15(23), 5568; https://doi.org/10.3390/rs15235568 - 30 Nov 2023
Viewed by 1162
Abstract
The characteristics of sea ice leads (SILs) in the Weddell Sea are an important basis for understanding the mechanism of the atmosphere–ocean system in the Southern Ocean. In this study, we derived the sea ice surface temperature (IST) of the Weddell Sea from [...] Read more.
The characteristics of sea ice leads (SILs) in the Weddell Sea are an important basis for understanding the mechanism of the atmosphere–ocean system in the Southern Ocean. In this study, we derived the sea ice surface temperature (IST) of the Weddell Sea from MODIS thermal images and then generated a daily SIL map for 2015 and 2022 by utilizing the iterative threshold method on the optimised MOD35 cloud-masked IST. The results showed that SIL variations in the Weddell Sea presented remarkable seasonal characteristics. The trend of the SIL area exhibited an initial rise followed by a decline from January to December, characterised by lower values in spring and summer and higher values in fall and winter. SILs in the Weddell Sea were predominantly concentrated between 70~78°S and 60~30°W. The coastal spatial distribution density of the SILs exceeded that of offshore regions, peaking near the Antarctic Peninsula and then near Queen Maud Land. The SIL variation was mainly influenced by dynamical factors, and there were strong positive correlations between the wind field, ocean currents, and sea-ice motion. Full article
(This article belongs to the Special Issue Remote Sensing of Polar Sea Ice)
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Graphical abstract

Graphical abstract
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<p>Flowchart of SIL identification algorithm [<a href="#B28-remotesensing-15-05568" class="html-bibr">28</a>,<a href="#B30-remotesensing-15-05568" class="html-bibr">30</a>].</p>
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<p>Distribution of Sentinel-2 data used in this study (the label corresponds the serial number in <a href="#remotesensing-15-05568-f003" class="html-fig">Figure 3</a>).</p>
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<p>Comparison of SILs detected from Sentinel-2 and MODIS.</p>
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<p>Temporal SILA variation in 2015 and 2022.</p>
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<p>The SILA of the Weddell Sea in the latitudinal and longitudinal directions, where (<b>a</b>,<b>b</b>) represent the latitudinal and longitudinal results of 2015, and (<b>c</b>,<b>d</b>) represent the latitudinal and longitudinal results of 2022. (Grey colour represents the missing data).</p>
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<p>Spatial distribution of the leads for the period of June to September in 2015 and 2022.</p>
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<p>Spatial distribution of the leads for the period of June to September in 2015 and 2022.</p>
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<p>Observed lead frequencies based on daily composites for the period of April to September, where (<b>a</b>,<b>b</b>) represent the relative frequency of 2015 and 2022, respectively, (<b>c</b>) represents the mean relative frequency of 2015 and 2022, and (<b>d</b>) represents the mean relative frequency from 2003 to 2019 according to Reiser’s dataset. The arrows indicate the similarity of the comparison.</p>
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<p>Correlation coefficients between factors in 2015 (<b>a</b>) and 2022 (<b>b</b>). (SILA: sea ice leads area, T2M: 2 m air temperature, SLP: mean sea level pressure, WIND: 10 m wind speed, SIM: sea ice motion speed, CUR: ocean current speed. * denotes a significance level of 0.05, ** denotes a significance level of 0.01 and *** denotes a significance level of 0.001).</p>
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<p>Changes in SIE and 2 m temperature in 2015 (<b>a</b>) and 2022 (<b>c</b>); changes in SIE and SILA in 2015 (<b>b</b>) and 2022 (<b>d</b>).</p>
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<p>Spatial correlation between SILA and the velocity of sea ice motion (<b>a</b>), wind speed (<b>c</b>), and current speed (<b>e</b>) in 2015, and the right columns (<b>b</b>,<b>d</b>,<b>f</b>) are the corresponding factors in 2022. (The pixels with correlation coefficients (r) greater than 0.6 exhibit <span class="html-italic">p</span> values below 0.05).</p>
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<p>The speed and direction map of sea ice motion (<b>a</b>), wind field (<b>c</b>), and ocean current (<b>e</b>) in 2015, and the right columns (<b>b</b>,<b>d</b>,<b>f</b>) are the corresponding factors in 2022.</p>
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<p>Temporal cloud cover variation in 2015 and 2022.</p>
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<p>Temporal lead fraction variation in 2015 and 2022.</p>
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21 pages, 11272 KiB  
Article
Decadal Changes in the Antarctic Sea Ice Response to the Changing ENSO in the Last Four Decades
by Young-Kwon Lim, Dong L. Wu, Kyu-Myong Kim and Jae N. Lee
Atmosphere 2023, 14(11), 1659; https://doi.org/10.3390/atmos14111659 - 6 Nov 2023
Cited by 1 | Viewed by 1898
Abstract
Sea ice fraction (SIF) over the Ross/Amundsen/Bellingshausen Sea (RAB) are investigated using the Modern-Era Retrospective Analysis for Research and Application, Version 2 (MERRA-2), focusing on the differences in time-lagged response to ENSO between the late 20th (1980–2000, L20) and the early 21st century [...] Read more.
Sea ice fraction (SIF) over the Ross/Amundsen/Bellingshausen Sea (RAB) are investigated using the Modern-Era Retrospective Analysis for Research and Application, Version 2 (MERRA-2), focusing on the differences in time-lagged response to ENSO between the late 20th (1980–2000, L20) and the early 21st century (2001–2021, E21). The findings suggest that the typical Antarctic response to ENSO is influenced by changes in ENSO type/intensity, highlighting the need for caution when investigating the Antarctic teleconnection. Time-lagged regressions onto the mature phase of El Niño reveal that the SIF decrease and SST increase over the RAB is relatively weaker in E21 and most pronounced at 0–4 months lag. Conversely, the SIF in L20 continues to decline and reaches its peak at two-season lag (5–7 months). Tropospheric wind, pressure, and wave activity in response to El Niño in L20 show a zonally oriented high/low-pressure areas with two-season lag, enhancing the poleward flow that plays a key role in sea ice melt in the RAB, while this pattern in E21 is insignificant at the same lag. This study suggests that stronger (weaker) and more eastern (central) Pacific ENSOs on average in L20 (E21) are associated with this decadal change in the SIF response to ENSO. Full article
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Figure 1

Figure 1
<p>(<b>a</b>) Time series of annual mean NSIDC sea ice extent anomaly (million km<sup>2</sup>) averaged over the entire Southern Hemisphere. (<b>b</b>) Time series of MERRA-2 sea ice fraction anomaly (%) for the Ross–Amundsen–Bellingshausen (RAB) Sea denoted by blue line and for the Weddell Sea denoted by red line. (<b>c</b>) Moving correlations between the sea ice fraction from RAB and Weddell Sea shown in (<b>b</b>). The 21-year sliding window is applied to calculate the moving correlations. In three panels, green dashed lines represent the zero line. A significant change in correlation is evident when comparing the 20th century (with a negative correlation) to the 21st century (exhibiting a positive correlation). According to a <span class="html-italic">t</span>-test with N-2 degrees of freedom (where N equals 21), the critical correlation value at a 95% confidence level is approximately ±0.43.</p>
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<p>Annual mean anomalies of sea surface temperature (shaded) and horizontal wind vectors at 2 m level (m s<sup>−1</sup>) regressed onto the sea ice fraction over the Ross–Amundsen–Bellingshausen (RAB) Sea (left panel) and over the Weddell Sea (right panel). Upper panel (<b>a</b>,<b>c</b>) represents the result for the late 20th century (L20, 1980–2000) period, while lower panel (<b>b</b>,<b>d</b>) is the result for the early 21st century (E21, 2001–2021) period that has overall weaker regressed anomalies than the L20 period. Yellow stippled areas indicate that the anomalies are statistically significant at 95% confidence levels.</p>
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<p>Time-lagged moving correlations over the 1980–2021 period applying a sliding window spanning 21 years. The left panel (<b>a</b>) shows the correlations between the Niño3.4 SST and SST over the Ross–Amundsen–Bellingshausen (RAB) Sea while the SST over the RAB is switched to sea ice fraction in the right panel (<b>b</b>). Results are plotted in red lines for time-lags of 1 to 4 months and blue lines for time-lags of 5 to 7 months. The critical correlation value at a 95% confidence level, based on a <span class="html-italic">t</span>-test with N-2 degrees of freedom (where N equals 21), is approximately ±0.43. Positive relationship between the two SSTs in the left panel and negative relationship between the Niño3.4 SST and sea ice fraction in RAB is stronger in the late 20th century. Amplitudes of correlations are also larger for longer time-lag in the late 20th century, while that feature is not evident in the early 21st century.</p>
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<p>Composite of the detrended SST anomaly with respect to El Niño (upper panel, (<b>a</b>,<b>c</b>)) and La Niña (lower panel, (<b>b</b>,<b>d</b>)). The left panel (<b>a</b>,<b>b</b>) represents the result for the late 20th century while the right panel (<b>c</b>,<b>d</b>) is the result for the early 21st century period. The amplitude of anomaly in the tropical Pacific (ENSO occurrence region) is clearly larger for the late 20th century. Green stippled areas indicate that the anomalies are statistically significant at 95% confidence levels.</p>
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<p>Distribution of SST (green contoured) and sea ice fraction (shaded) anomaly regressed onto the boreal winter Niño3.4 SST (averaged for December, January, and February). Each panel from the left to the right represents the regressed distributions considering 0, 1, 2, and 3 season lag. Results for the late 20th century are shown on the upper panel (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) while the lower panel (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) corresponds to the result for the early 21st century. The regressed anomaly over the Ross–Amundsen–Bellingshausen Sea reaches the maximum at 2 season lag in the late 20th century, while the amplitude gets weak at the same lag in the early 21st century. Black stippled areas indicate that the anomalies are statistically significant at 95% confidence levels.</p>
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<p>The number of grid points (y-axis) with respect to the regressed sea ice fraction (SIF) anomaly values (%, x-axis) distributed over the longitudes encompassing the Ross, Amundsen, Bellingshausen, and Weddell Sea. The regression is conducted onto the boreal winter Niño3.4 SST. Different colored lines (blue, red, and green) illustrate the outcomes for regressions at lags of 0–4 months, 5–7 months, and 8–11 months. In the left panel (<b>a</b>), the focus is on the late 20th century, while the right panel (<b>b</b>) pertains to the early 21st century. The most significant decrease in SIF occurs at a lag of 5–7 months (indicated by the red lines) during the late 20th century (left panel, (<b>a</b>)).</p>
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<p>Same as <a href="#atmosphere-14-01659-f005" class="html-fig">Figure 5</a> but for regressed anomalies of 2 m level meridional flow (shaded) and sea level pressure (green contoured). Results for the late 20th century are displayed in the upper panel ((<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>)) while the lower panel ((<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>)) represents the result for the early 21st century. Specifically, time-lagged response lasts longer and the meridional flow over the Ross–Amundsen–Bellingshausen Sea reaches the maximum in the late 20th century (<b>e</b>), while the lagged response gets weak at the same lag in the early 21st century (<b>f</b>).</p>
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<p>Shaded (<b>b,c</b>): Latitude (x-axis)-time-lag (y-axis) cross-section of the 2 m level meridional wind anomalies regressed onto the boreal winter Niño3.4 SST. X-axis denotes the latitude from 90° S to 45° S and y-axis denotes the time-lag ranging from 0 to 11 months. The left panel (<b>b</b>) represents the result for the late 20th century while the right panel (<b>c</b>) is the result for the early 21st century period. Longitudinal width for averaging the meridional winds is 150–120° W that covers the Ross and Amundsen Sea for the late 20th century (left (<b>b</b>)) and 135–105° W that covers the Amundsen and Bellingshausen Sea for the early 21st century (right (<b>c</b>)). Green stippled areas indicate that the anomalies are statistically significant at 95% confidence levels. Time series in black (<b>a,d</b>): Regressed sea ice fraction anomaly over the Ross and Amundsen Sea in the late 20th century (left (<b>a</b>)) and the Amundsen and Bellingshausen Sea in the early 21st century (right (<b>d</b>)), respectively, as a function of time-lag from 0 to 11 months. X-axis denotes the regressed anomaly in percentage. Green dashed lines in panels (<b>a</b>,<b>d</b>) represent the zero line. The contrast is evident: during the late 20th century (<b>a,b</b>), the prevailing negative wind anomaly (poleward northly flow depicted by blue shading) is most pronounced at lags of 5–7 months, resulting in the greatest reduction in sea ice at those lags. Conversely, in the early 21st century (<b>c,d</b>), the most robust poleward northerly flow is seen at lags of 0–4 months, aligning with the highest sea ice reduction at those lags.</p>
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<p>Distributions of 300 hPa level wind (vector) and geopotential height (shaded) anomalies regressed onto the boreal winter Niño3.4 SST. Time-lagged regression of the wind and geopotential height is conducted considering time-lags of 0 to 11 months onto the boreal winter Niño3.4 SST. The first ((<b>a</b>–<b>c</b>,<b>m</b>–<b>o</b>)) and the third column ((<b>g</b>–<b>i</b>,<b>s</b>–<b>u</b>)) from the left represent the results for the late 20th century, and the results for the early 21st century are shown in the second ((<b>d</b>–<b>f</b>,<b>p</b>–<b>r</b>)) and the fourth column ((<b>j</b>–<b>l</b>,<b>v</b>–<b>x</b>)). Specifically, the northwest–southeast-oriented regressed anomalies in the Pacific at lags of 0–3 months ((<b>a</b>–<b>c</b>,<b>g</b>)), followed by zonally oriented high/low-pressure anomalies remaining active at lags of 5–7 months ((<b>i</b>,<b>m,n</b>)), are dominant in the 20th century, while the regressed anomalies weaken earlier and are no longer evident after a 5-month (<b>p</b>–<b>r</b>) lag in the 21st century.</p>
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<p>Distribution of 300 hPa geopotential height (shaded) and wave activity flux vectors regressed onto the boreal winter Niño3.4 SST. In each set of panels, moving from left to right, the depicted distributions are the regressed distributions considering time-lags of 0, 1, 2, and 3 seasons. The upper panel (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) displays results for the late 20th century, while the lower panel (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) exhibits results for the early 21st century. Yellow contour lines denote the wave source region in the tropical Pacific based on the Rossby wave source calculation. Specifically, a prevailing northwest–southeast-aligned Rossby wave train is prominent at lags of 0 and 1 season during the 20th century (upper-panel, (<b>a</b>,<b>c</b>)), followed by wave flux vectors aligned more zonally at a lag of 2 seasons (<b>e</b>), while the wave train is notably insignificant at that lag in the 21st century (lower panel, (<b>f</b>)).</p>
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<p>Latitude–height cross-section of the vertical pressure velocity at each pressure level regressed onto the boreal winter Niño3.4 SST. Longitudinal width for averaging is 180–90° W that encompasses the central to eastern Pacific. The left panel (<b>a</b>–<b>c</b>) is the result for time-lag of 0 to 2 seasons in the late 20th century while the right panel (<b>d</b>–<b>f</b>) represents the result for the early 21st century period. Green stippled areas indicate that the anomalies are statistically significant at 95% confidence levels. Vertical circulation cells spanning from the tropical Pacific to Antarctica is well-structured during the late 20th century (left, (<b>a</b>–<b>c</b>)), while the vertical structure is less well-organized in the early 21st century (<b>d</b>–<b>f</b>).</p>
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<p>Time series of annual mean NSIDC sea ice extent anomaly (million km<sup>2</sup>) (black) and MERRA-2 sea ice fraction anomaly (blue) averaged over the entire Southern Hemisphere.</p>
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<p>Distribution of ERA5 sea ice fraction (shaded) anomaly regressed onto the boreal winter Niño3.4 SST. Each panel from the left to the right represents the regressed distributions for 0, 1, 2, and 3 season lag. Results for the late 20th century are shown on the upper panel ((<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>)) while the lower panel ((<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>)) corresponds to the result for the early 21st century.</p>
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18 pages, 7307 KiB  
Article
Changes in the Antarctic’s Summer Surface Albedo, Observed by Satellite since 1982 and Associated with Sea Ice Anomalies
by Yuqi Sun, Yetang Wang, Zhaosheng Zhai and Min Zhou
Remote Sens. 2023, 15(20), 4940; https://doi.org/10.3390/rs15204940 - 12 Oct 2023
Viewed by 1314
Abstract
In polar regions, positive feedback of snow and ice albedo can intensify global warming. While recent significant decreases in Arctic surface ice albedo have drawn considerable attention, Antarctic surface albedo variability remains underexplored. Here, satellite albedo product CLARA-A2.1-SAL is first validated and then [...] Read more.
In polar regions, positive feedback of snow and ice albedo can intensify global warming. While recent significant decreases in Arctic surface ice albedo have drawn considerable attention, Antarctic surface albedo variability remains underexplored. Here, satellite albedo product CLARA-A2.1-SAL is first validated and then used to investigate spatial and temporal trends in the summer albedo over the Antarctic from 1982 to 2018, along with their association with Antarctic sea ice changes. The SAL product matches well surface albedo observations from eight stations, suggesting its robust performance in Antarctica. Summer surface albedo averaged over the entire ice sheet shows a downward trend since 1982, albeit not statistically significant. In contrast, a significant upward trend is observed in the sea ice region. Spatially, for ice sheet surface albedo, positive trends occur in the eastern Antarctica Peninsula and the margins of East Antarctica, whereas other regions exhibit negative trends, most prominently in the Ross and Ronne ice shelves. For sea ice albedo, positive trends are observed in the Ross Sea and the Weddell Sea, but negative trends are observed in the Bellingshausen and the Amundsen Seas. Between 2016 and 2018, an unusual decrease in the sea ice extent significantly affected both sea ice and Antarctic ice sheet (AIS) surface albedo changes. However, for the 1982–2015 period, while the effect of sea ice on its own albedo is significant, its impact on ice sheet albedo is less apparent. Air temperature and snow depth also contribute much to sea ice albedo changes. However, on ice sheet surface albedo, the influence of temperature and snow accumulation appears limited. Full article
(This article belongs to the Special Issue New Insights in Remote Sensing of Snow and Glaciers)
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Graphical abstract

Graphical abstract
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<p>Spatial distribution of meteorological and BSRN stations and the boundaries (bold black lines) of sea ice regions around Antarctica.</p>
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<p>A flowchart of this study.</p>
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<p>The MB and RMSE values of the monthly (<b>a</b>,<b>c</b>) and pentad mean (<b>b</b>,<b>d</b>) of the CLARA-A2.1-SAL product at eight stations in austral summer.</p>
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<p>Monthly surface albedo from in situ observations and the CLARA-A2.1-SAL product at the meteorological stations on the Antarctica ice sheet in austral summer. Blue and purple points represent in situ observations and CLARA-A2.1-SAL albedo, respectively.</p>
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<p>As for <a href="#remotesensing-15-04940-f004" class="html-fig">Figure 4</a>, but for pentad surface albedo. Blue and purple points represent in situ observations and CLARA-A2.1-SAL albedo, respectively.</p>
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<p>Interannual variability in (<b>a</b>) AIS and (<b>b</b>) Antarctic sea ice summer mean albedo between 1982 and 2018. Solid black and green lines represent the albedo changes between 1982 and 2018 and 1982 and 2015, respectively, and dashed black and green lines are the corresponding linear trends.</p>
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<p>Spatial distribution of trends in summer surface albedo over (<b>a</b>) the Antarctic continent and (<b>b</b>) the sea ice from 1982 to 2018. Standard deviations of summer mean albedo between 1982 and 2018 on the (<b>c</b>) AIS surface and (<b>d</b>) sea ice. Black shadows in panels a and b indicate that the trends are significant at the confidence level of 95%.</p>
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<p>Spatial distribution of correlation coefficients (<span class="html-italic">R</span>) between SIE in each sector and Antarctic albedo in the austral summer at different time periods. The smaller angle between the two black lines corresponds to the SIE in each sector, which in turn is the (<b>a</b>,<b>d</b>) Ross Sea, (<b>b</b>,<b>e</b>) Bellingshausen and Amundsen Sea, and (<b>c</b>,<b>f</b>) Weddell Sea. (<b>a</b>–<b>c</b>) are the period 1982–2018, (<b>d</b>–<b>f</b>) are the period 1982–2015. The black shadow indicates that the correlations at each grid pixel are significant at the 95% confidence level.</p>
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<p>Anomalies of averaged SIC (<b>a</b>,<b>b</b>) and surface albedo (<b>c</b>,<b>d</b>) over the Antarctic continent and sea ice areas in the austral summer between 2013 and 2015 and 2016 and 2018, respectively, relative to the 1982–2011 mean. (<b>a</b>,<b>c</b>) are the period 2013–2015, and (<b>b</b>,<b>d</b>) are the period 2016–2018.</p>
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<p>Spatial correlations between summer mean albedo and (<b>a</b>) air temperature, (<b>b</b>) snow depth, and (<b>c</b>) CFC over Antarctic sea ice from 1982 to 2018. The correlation at the pixels covered by black shadow is significant at the 95% confidence level.</p>
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<p>Spatial correlations between summer mean albedo and (<b>a</b>) surface melting days, (<b>b</b>) air temperature, and (<b>c</b>) snow accumulation (P-E) over the AIS from 1982 to 2018. The four rectangles in (<b>a</b>–<b>c</b>) are magnifications of the AIS margin. The correlations at the pixels covered by black shadow are significant at the 95% confidence level.</p>
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20 pages, 5188 KiB  
Article
Echinoids and Crinoids from Terra Nova Bay (Ross Sea) Based on a Reverse Taxonomy Approach
by Alice Guzzi, Maria Chiara Alvaro, Matteo Cecchetto and Stefano Schiaparelli
Diversity 2023, 15(7), 875; https://doi.org/10.3390/d15070875 - 21 Jul 2023
Cited by 2 | Viewed by 1816
Abstract
The identification of species present in an ecosystem and the assessment of a faunistic inventory is the first step in any ecological survey and conservation effort. Thanks to technological progress, DNA barcoding has sped up species identification and is a great support to [...] Read more.
The identification of species present in an ecosystem and the assessment of a faunistic inventory is the first step in any ecological survey and conservation effort. Thanks to technological progress, DNA barcoding has sped up species identification and is a great support to morphological taxonomy. In this work, we used a “Reverse Taxonomy” approach, where molecular (DNA barcoding) analyses were followed by morphological (skeletal features) ones to determine the specific status of 70 echinoid and 22 crinoid specimens, collected during eight different expeditions in the Ross and Weddell Seas. Of a total of 13 species of sea urchins, 6 were from the Terra Nova Bay area (TNB, Ross Sea) and 4 crinoids were identified. Previous scientific literature reported only four species of sea urchins from TNB to which we added the first records of Abatus cordatus (Verrill, 1876), Abatus curvidens Mortensen, 1936 and Abatus ingens Koehler, 1926. Moreover, we found a previous misidentification of Abatus koehleri (Thiéry, 1909), erroneously reported as A. elongatus in a scientific publication for the area. All the crinoid records are new for the area as there was no previous faunistic inventory available for TNB. Full article
(This article belongs to the Special Issue Ecology and Biogeography of Marine Benthos)
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Figure 1

Figure 1
<p>Antarctica (high left); highlighted in red is the Antarctic Peninsula and in blue the Terra Nova Bay (Ross Sea) sector. (<b>A</b>) Sampling station of the Antarctic Peninsula and (<b>B</b>) sampling sites in Terra Nova Bay with Mario Zucchelli Station (Italy) highlighted in green square. Legend is colour coded for expedition.</p>
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<p>Tree topology comparison of maximum likelihood. Posterior probability node values are shown on the tree with corresponding legend for each analysis. BIN: barcode index number; BOLD: automatic species delimitation [<a href="#B58-diversity-15-00875" class="html-bibr">58</a>]; ABGD: results from automatic barcode gap discovery method [<a href="#B16-diversity-15-00875" class="html-bibr">16</a>]; GMYC: species delimitation from generalized mixed Yule coalescent method [<a href="#B59-diversity-15-00875" class="html-bibr">59</a>]; bPTP: species delimitation using Bayesian Poisson tree processes method [<a href="#B62-diversity-15-00875" class="html-bibr">62</a>].</p>
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<p>Representative photos of selected specimens. In the tree, the different species identified are highlighted by different colours. The species present in the Terra Nova Bay area are listed on the right. Bottom left is the schematic view of the tree in <a href="#diversity-15-00875-f002" class="html-fig">Figure 2</a>, the portion analysed in detail in the image is highlighted in red. Scale bar: 1 cm in grey.</p>
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<p>Representative photos of selected specimens (left—aboral view, right—oral view) from Terra Nova Bay. In the tree, the different species identified are highlighted by different colours (only the species from TNB are figured). Bottom left is the schematic view of the tree in <a href="#diversity-15-00875-f002" class="html-fig">Figure 2</a>, the portion analysed in detail in the image is highlighted in red. Scale bar: 1 cm in grey.</p>
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<p>List of echinoids species found in Terra Nova Bay and updated depth range [<a href="#B38-diversity-15-00875" class="html-bibr">38</a>].</p>
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19 pages, 3122 KiB  
Article
Recent Changes in Composition and Distribution Patterns of Summer Mesozooplankton off the Western Antarctic Peninsula
by Valentina V. Kasyan
Water 2023, 15(10), 1948; https://doi.org/10.3390/w15101948 - 21 May 2023
Cited by 3 | Viewed by 2034
Abstract
The Southern Ocean has undergone significant climate-related changes in recent decades. As a result, pelagic communities inhabiting these waters, particularly mesozooplankton, have adapted to new conditions. The present study considers the patterns of horizontal and vertical (up to 1000 m) distribution, the composition, [...] Read more.
The Southern Ocean has undergone significant climate-related changes in recent decades. As a result, pelagic communities inhabiting these waters, particularly mesozooplankton, have adapted to new conditions. The present study considers the patterns of horizontal and vertical (up to 1000 m) distribution, the composition, abundance, and biomass of mesozooplankton, and the relationships of these parameters to the extreme environmental conditions off the western Antarctic Peninsula throughout the record-warm austral summer season of 2022. Sampling was conducted using the opening/closing Multinet system (0.25 m2 aperture) equipped with five 150-μm mesh nets and a WP-2 net. The mesozooplankton was represented by the three most abundant groups: eggs and larvae of euphausiids such as Euphausia superba, small copepods such as Oithona similis, and large calanoid copepods such as Calanoides acutus, Calanus propinquus, Metridia gerlachei, and Rhincalanus gigas. The composition and quantitative distribution of the mesozooplankton significantly varied: the copepods were abundant in the west, off the Antarctic Peninsula, while eggs and larvae of euphausiids were abundant in the east, off the South Orkney Islands. Most mesozooplankton occurred in the upper 200 m layer, and each taxon showed characteristic depth preference: small copepods, euphausiids larvae, and cirripeds cypris larvae were abundant in the epipelagic layer, while large calanoid copepods, euphausiids eggs, amphipods, pelagic polychaetes, and ostracods were found mostly in the mesopelagic layer. The composition and quantitative distribution of mesozooplankton had clear relationships with environmental factors, particularly with a combination of variables such as water salinity, temperature, and chlorophyll a concentration. Full article
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Figure 1
<p>Map of the sampling stations and the main ocean currents designated according to [<a href="#B20-water-15-01948" class="html-bibr">20</a>,<a href="#B48-water-15-01948" class="html-bibr">48</a>,<a href="#B49-water-15-01948" class="html-bibr">49</a>,<a href="#B50-water-15-01948" class="html-bibr">50</a>]. Numerals are codes of the stations. Circles indicate plankton nets used at the stations: red is the Multinet, and yellow is WP-2. Dashed lines indicate currents in the study region: ACC is the Antarctic Circumpolar Current; BC, the Bransfield Current; ACoC, the Antarctic Coastal Current; ASF, the Antarctic Slope Front; WF, the Weddell Front.</p>
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<p>Values of water temperature (<b>a</b>), salinity (<b>b</b>), oxygen concentration (<b>c</b>), and chlorophyll <span class="html-italic">a</span> concentration (<b>d</b>) in the 50-, 100-, 200-, 500-, and 1000 m layers during the austral summer of 2022.</p>
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<p>The total abundance (<b>a</b>) and contribution of the major taxa (<b>b</b>) of mesozooplankton in the Bransfield Strait (BS), Antarctic Sound (AS), Weddell Sea (WS), and off the South Orkney Islands (SOI) during the austral summer 2022.</p>
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<p>The total biomass (<b>a</b>) and contribution of the major taxa (<b>b</b>) of mesozooplankton in the study region. For an explanation of acronyms, see <a href="#water-15-01948-f003" class="html-fig">Figure 3</a>.</p>
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<p>Total copepod abundance (<b>a</b>) and biomass (<b>b</b>) during the austral summer of 2022. For an explanation of acronyms, see <a href="#water-15-01948-f003" class="html-fig">Figure 3</a>.</p>
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<p>Vertical distribution (up to 1000 m) of the average abundance of mesozooplankton (<b>a</b>) and contribution of the major species/taxa (abundance, ind. m<sup>−3</sup> and proportions, %) (<b>b</b>) off the western Antarctic Peninsula during the austral summer of 2022.</p>
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<p>Vertical distribution of average abundances of mesozooplankton (ind. m<sup>−3</sup>): (<b>a</b>) in the middle of the Bransfield Strait (BS); (<b>b</b>) in the Bransfield Strait off the South Shetland Islands; (<b>c</b>) in the deep-sea waters of the Antarctic Sound (AS); (<b>d</b>) in the southwestern Powell Basin (WS); (<b>e</b>) in the northeastern Powell Basin (WS); (<b>f</b>) in the coastal waters off the South Orkney Islands (SOI); (<b>g</b>) in the deep-sea waters off the South Orkney Islands (SOI).</p>
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<p>nMDS ordination plot (<b>a</b>) based on abundance of mesozooplankton by the Bray–Curtis similarity matrix, and location of the groups of stations in the inner (ISAP, green circles) and outer (OSAP, blue circles) sectors off the western Antarctic Peninsula (<b>b</b>). Blank circles mean non-recognized group.</p>
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<p>Canonical correspondence analysis (CCA) of mesozooplankton composition (in red) in relationships with the environmental variables (in blue). The symbols of the mesozooplankton communities (circles), as they were disclosed by nMDS analysis (see <a href="#water-15-01948-f008" class="html-fig">Figure 8</a>), were superimposed on the station labels. Abbreviations of the taxa are as follows: large copepods (LCop), small copepods (SCop), ostracods (Ostr), <span class="html-italic">cirripeds</span> cypris larvae (Cirrip), pelagic tunicates (Salp), amphipods (Amph), chaetognaths (Chaet), and euphausiid (Euph) eggs and larvae. For explanations for abbreviations of environmental variables, see <a href="#water-15-01948-t001" class="html-table">Table 1</a>.</p>
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14 pages, 1857 KiB  
Review
Animal–Energy Relationships in a Changing Ocean: The Case of Continental Shelf Macrobenthic Communities on the Weddell Sea and the Vicinity of the Antarctic Peninsula
by Enrique Isla
Biology 2023, 12(5), 659; https://doi.org/10.3390/biology12050659 - 27 Apr 2023
Cited by 1 | Viewed by 1728
Abstract
The continental shelves of the Weddell Sea and the Antarctic Peninsula vicinity host abundant macrobenthic communities, and the persistence of which is facing serious global change threats. The current relationship among pelagic energy production, its distribution over the shelf, and macrobenthic consumption is [...] Read more.
The continental shelves of the Weddell Sea and the Antarctic Peninsula vicinity host abundant macrobenthic communities, and the persistence of which is facing serious global change threats. The current relationship among pelagic energy production, its distribution over the shelf, and macrobenthic consumption is a “clockwork” mechanism that has evolved over thousands of years. Together with biological processes such as production, consumption, reproduction, and competence, it also involves ice (e.g., sea ice, ice shelves, and icebergs), wind, and water currents, among the most important physical controls. This bio-physical machinery undergoes environmental changes that most likely will compromise the persistence of the valuable biodiversity pool that Antarctic macrobenthic communities host. Scientific evidence shows that ongoing environmental change leads to primary production increases and also suggests that, in contrast, macrobenthic biomass and the organic carbon concentration in the sediment may decrease. Warming and acidification may affect the existence of the current Weddell Sea and Antarctic Peninsula shelf macrobenthic communities earlier than other global change agents. Species with the ability to cope with warmer water may have a greater chance of persisting together with allochthonous colonizers. The Antarctic macrobenthos biodiversity pool is a valuable ecosystem service that is under serious threat, and establishing marine protected areas may not be sufficient to preserve it. Full article
(This article belongs to the Special Issue Polar Ecosystem: Response of Organisms to Changing Climate)
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<p>(<b>a</b>) Study area showing (1) the continental shelf off the South Shetland Islands Archipelago at the Drake Passage, (2) the Bransfield Strait, (3) the Gerlache Strait, (4) the Antarctic Peninsula, (5) the Filchner Trough, (6) Cape Norvegia, and (7) the Austasen region. Each bathymetric isoline represents 500 m water depth. Map developed with QGIS software v. 3.8 and the International Bathymetric Chart of the Southern Ocean (IBCSO) shape files [<a href="#B44-biology-12-00659" class="html-bibr">44</a>]. (<b>b</b>) Organic carbon content (weight %) in continental shelf sediment below the upper mixed layer (below the layer where physical and biological disturbance is more intense). The upper mixed layer thickness varied between 0 and 10 cm for the different stations. AUS, FTE, FTX, FTS, FTW, LAR, NWW, BFS, and DPS stand for Austasen, Filchner Trough East, Axis (X), Slope (S), West (W), Larsen, Northwestern Weddell Sea, Bransfield Strait, and Drake Passage, respectively. Map modified after Isla, 2020 [<a href="#B45-biology-12-00659" class="html-bibr">45</a>]. Yellow dots show sampling stations.</p>
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14 pages, 3233 KiB  
Article
Oxygen and pCO2 in the Surface Waters of the Atlantic Southern Ocean in 2021–2022
by Natalia A. Orekhova, Sergey K. Konovalov, Alexander A. Polukhin and Anna M. Seliverstova
Water 2023, 15(9), 1642; https://doi.org/10.3390/w15091642 - 23 Apr 2023
Cited by 2 | Viewed by 2564
Abstract
The carbon dioxide concentration in the atmosphere has progressively risen since pre-industrial times. About one-third of the anthropogenically generated CO2 is absorbed by the waters of the World Ocean, whereas the waters of the Southern Ocean take up about 40% of this [...] Read more.
The carbon dioxide concentration in the atmosphere has progressively risen since pre-industrial times. About one-third of the anthropogenically generated CO2 is absorbed by the waters of the World Ocean, whereas the waters of the Southern Ocean take up about 40% of this CO2. The concentrations of oxygen and carbon dioxide dissolved in seawater are sensitive to climate changes, transferring anthropogenic pressures with consequences for the biogeochemical cycles in the World Ocean. The Southern Ocean is a key region for the exchange of oxygen and carbon between the surface water and the atmosphere and for their transfer with cold water masses to the deep layers of the Ocean. In this paper, we discuss the dynamics of the carbon dioxide partial pressure (pCO2) and dissolved oxygen (O2) in the surface waters of the Atlantic Southern Ocean based on data collected during the 87th cruise of the R/V “Academik Mstislav Keldysh”. The study area includes the Bransfield Strait, Antarctic Sound, the Powell Basin, the Weddell, and Scotia Seas. We have analyzed the spatial distribution of pCO2 and oxygen for the areas of transformation of water masses and changes in biogeochemical processes. In the zone of Scotia and Weddell Seas, we have observed an increase in pCO2 and a decrease in oxygen concentrations at the transect from the Weddell Sea at 56° W to the Powell Basin. From the Antarctic Sound to the Bransfield Strait, a decrease in oxygen saturation and an increase in pCO2 has been traced. The surface waters of the Bransfield Strait have revealed the greatest variability of hydrochemical characteristics due to a complex structure of currents and intrusions of different water masses. In general, this area has been characterized by the maximum pCO2, while the surface waters are undersaturated with oxygen. The variability of the AOU/ΔpCO2 (w-a) ratio has revealed a pCO2 oversaturation and an O2 undersaturation in the waters of the Bransfield Strait. It is evidence of active organic carbon decomposition as the major controlling process. Yet, photosynthesis is the major biogeochemical process in the studied areas of the Weddell and Scotia seas, and their waters have been undersaturated with pCO2 and oversaturated with O2. As it comes from the analysis of the distribution and correlation coefficients of AOU and the sea-air gradient of pCO2 with other physical and biogeochemical properties, the predominance of the biotic processes to the dynamics of O2 and pCO2 in the surface water layer has been demonstrated for the studied areas. Yet, there is evidence of additional sources of CO2 not associated with the production and destruction processes of organic matter. Full article
(This article belongs to the Section Oceans and Coastal Zones)
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<p>Locations of oceanographic stations in the 87th cruise of the R/V “A. M. Keldysh”.</p>
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<p>Spatial variation in oxygen saturation (<b>a</b>) and pCO<sub>2</sub> concentration (<b>b</b>) at the surface waters of the transect from 56° W to the Orkney Islands.</p>
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<p>Spatial variation in oxygen saturation (<b>a</b>) and pCO<sub>2</sub> concentration (<b>b</b>) at the surface waters of the Antarctic Sound.</p>
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<p>Spatial variation in oxygen saturation (<b>a</b>) and pCO<sub>2</sub> concentration (<b>b</b>) at the surface waters of the Bransfield Strait.</p>
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<p>AOU (<b>a</b>) and ΔpCO<sub>2</sub> (<b>b</b>) distribution depending on temperature and salinity.</p>
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<p>Ratio of pCO2 gradient to apparent oxygen consumption (AOU) in different areas.</p>
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<p>Areas with different water characteristics, identified by the ratio of ΔpCO<sub>2</sub>/AOU. The blue circles are the stations of the Area 1, the red circles are the stations of the Area 2, the black circles are the stations of the Area 3, the black triangles are the stations of the Area 4.</p>
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<p>Relationship between AOU (<b>a</b>) or ΔpCO<sub>2</sub> (<b>b</b>) and pH values.</p>
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28 pages, 3343 KiB  
Article
Antarctic Seabed Assemblages in an Ice-Shelf-Adjacent Polynya, Western Weddell Sea
by Bétina A. V. Frinault, Frazer D. W. Christie, Sarah E. Fawcett, Raquel F. Flynn, Katherine A. Hutchinson, Chloë M. J. Montes Strevens, Michelle L. Taylor, Lucy C. Woodall and David K. A. Barnes
Biology 2022, 11(12), 1705; https://doi.org/10.3390/biology11121705 - 25 Nov 2022
Cited by 2 | Viewed by 2865
Abstract
Ice shelves cover ~1.6 million km2 of the Antarctic continental shelf and are sensitive indicators of climate change. With ice-shelf retreat, aphotic marine environments transform into new open-water spaces of photo-induced primary production and associated organic matter export to the benthos. Predicting [...] Read more.
Ice shelves cover ~1.6 million km2 of the Antarctic continental shelf and are sensitive indicators of climate change. With ice-shelf retreat, aphotic marine environments transform into new open-water spaces of photo-induced primary production and associated organic matter export to the benthos. Predicting how Antarctic seafloor assemblages may develop following ice-shelf loss requires knowledge of assemblages bordering the ice-shelf margins, which are relatively undocumented. This study investigated seafloor assemblages, by taxa and functional groups, in a coastal polynya adjacent to the Larsen C Ice Shelf front, western Weddell Sea. The study area is rarely accessed, at the frontline of climate change, and located within a CCAMLR-proposed international marine protected area. Four sites, ~1 to 16 km from the ice-shelf front, were explored for megabenthic assemblages, and potential environmental drivers of assemblage structures were assessed. Faunal density increased with distance from the ice shelf, with epifaunal deposit-feeders a surrogate for overall density trends. Faunal richness did not exhibit a significant pattern with distance from the ice shelf and was most variable at sites closest to the ice-shelf front. Faunal assemblages significantly differed in composition among sites, and those nearest to the ice shelf were the most dissimilar; however, ice-shelf proximity did not emerge as a significant driver of assemblage structure. Overall, the study found a biologically-diverse and complex seafloor environment close to an ice-shelf front and provides ecological baselines for monitoring benthic ecosystem responses to environmental change, supporting marine management. Full article
(This article belongs to the Special Issue Polar Ecosystem: Response of Organisms to Changing Climate)
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<p>Study zone and sites. Inset (<b>A</b>) Antarctica indicating the Antarctic Peninsula and Weddell Sea; inset (<b>B</b>) Antarctic Peninsula indicating Larsen C Ice Shelf and the extent of the main map; and (<b>C</b>) main map of western Weddell Sea study area, showing the modern-day (2019) ice front and the locations of continental shelf study sites, LCP1-4 (black circles), investigated for benthic megafaunal assemblages. Light gray indicates ice shelves and darker gray indicates ice-covered land/grounded ice. Bathymetry is from the International Bathymetry Chart of the Southern Ocean v2 [<a href="#B79-biology-11-01705" class="html-bibr">79</a>], with 100 m contours. The ice-shelf fronts are from Cook and Vaughan [<a href="#B9-biology-11-01705" class="html-bibr">9</a>], Cook et al. [<a href="#B80-biology-11-01705" class="html-bibr">80</a>] and Christie et al. [<a href="#B81-biology-11-01705" class="html-bibr">81</a>].</p>
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<p>Ice-shelf-adjacent sites accumulate faunal richness differently. Morphotaxa richness with area surveyed for sites LCP1-4 (note, slopes for LCP1 and LCP4 were not significantly different from each other, <span class="html-italic">p</span> &gt; 0.2, therefore, datasets pooled). Significantly different slopes are shown (<span class="html-italic">p</span>-values for differences between slopes all &lt;0.01). For regression lines, F-values = 1546.39, 767.44, and 930.35, for LCP1 and LCP4 combined, LCP2, and LCP3, respectively, with <span class="html-italic">p</span>-values all &lt;0.001.</p>
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<p>Megafaunal density generally increases with distance from the ice shelf. Mean benthic megafaunal density (ind. m<sup>−2</sup>) (±SE) of each sample (three per site) with distance from the ice-shelf front (km). Mean density (±SE) calculated for each sample using associated frames (minimum of 44 frames per data point). Site LCP1 = diamonds, LCP2 = triangles, LCP3 = circles, and LCP4 = squares. Symbols filled in white excluded from regression statistics (hence trend-line generation) (owing to water mass differences, see [<a href="#B90-biology-11-01705" class="html-bibr">90</a>]).</p>
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<p>Megafaunal density generally decreases with increasing hardness of the substratum. Mean faunal densities (ind. m<sup>−2</sup>) (± SE) of samples with substratum type (scale of increasing hardness from left to right), where 1 = soft-sediment: mixed; 2 = mixed: soft-sediment; 3 = mixed: boulder; 4 = assorted hard: mixed; 5 = assorted hard: boulder/mixed; and 6 = assorted hard: boulder. Site LCP1 = diamonds, LCP2 = triangles, LCP3 = circles, and LCP4 = squares.</p>
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<p>Functional group densities can alter differently relative to ice-shelf proximity. Mean functional group densities (ind. m<sup>−2</sup>) (±SE) of samples with distance from the ice-shelf front (km), with an example taxon for each group (images provided by D.K.A.B.): (<b>A</b>) deposit-feeding crawlers; (<b>B</b>) pioneer sessile suspension feeders; (<b>C</b>) hard sessile predator/scavengers; (<b>D</b>) grazers; (<b>E</b>) hard deposit-feeders; and (<b>F</b>) soft sessile predator/scavengers. Site LCP1 = diamonds, LCP2 = triangles, LCP3 = circles, and LCP4 = squares; white-filled symbols indicate (due to water mass disparities, see [<a href="#B90-biology-11-01705" class="html-bibr">90</a>]) data not included in trend-line generation. R<sup>2</sup> values for (<b>A</b>–<b>C</b>,<b>F</b>) = 0.63, 0.85, 0.74 and 0.70, respectively, and regression statistics indicated all associated <span class="html-italic">p</span>-values ≤ 0.01.</p>
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<p>Deposit-feeding crawlers as an effective surrogate of the overall faunal density trend observed with distance from the ice shelf. Mean densities (ind. m<sup>−2</sup>) (±SE) with distance from the ice-shelf front (km) of: (<b>A</b>) overall fauna; (<b>B</b>) deposit-feeding crawlers; and (<b>C</b>) overall fauna excluding deposit-feeding crawlers. Site LCP1 = diamonds, LCP2 = triangles, LCP3 = circles, and LCP4 = squares; white-filled symbols indicate data not included in trend-line generation (because of water mass differences, see [<a href="#B90-biology-11-01705" class="html-bibr">90</a>]). R<sup>2</sup> values for (<b>A</b>–<b>C</b>) = 0.86, 0.63 and 0.85, respectively. Regression statistics for C presented a <span class="html-italic">p</span>-value of ≤0.001.</p>
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<p>Clustering of samples corresponds with respective study site. Dendrogram resulting from hierarchical cluster analysis of samples from Larsen C study sites (based on Bray–Curtis similarities and using group-average linking). Black continuous lines indicate significant clusters, identified by a SIMPROF test, and red dashed lines indicate non-significant clusters. Site LCP1 = diamonds, LCP2 = triangles, LCP3 = circles, and LCP4 = squares.</p>
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<p>Differences in the environmental setting of sites (and samples). PCA ordination showing spatial variation in environmental variables among the 12 samples. Site LCP1 = diamonds, LCP2 = triangles, LCP3 = circles, and LCP4 = squares. PC = Principal Component.</p>
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<p>Geographical regions accumulate faunal richness differently. Morphotaxa richness with area examined for Larsen C Polynya study sites, LCP1-4, compared to photographic studies from Ryder Bay (western Antarctic Peninsula, WAP), around the island of South Georgia Island (SG) and the Barents Sea (labeled here as “Arctic”) (sources of external datasets used are provided in the text).</p>
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30 pages, 5143 KiB  
Article
Composition and Distribution of Plankton Communities in the Atlantic Sector of the Southern Ocean
by Valentina V. Kasyan, Dmitrii G. Bitiutskii, Aleksej V. Mishin, Oleg A. Zuev, Svetlana A. Murzina, Philipp V. Sapozhnikov, Olga Yu. Kalinina, Vitaly L. Syomin, Glafira D. Kolbasova, Viktor P. Voronin, Elena S. Chudinovskikh and Alexei M. Orlov
Diversity 2022, 14(11), 923; https://doi.org/10.3390/d14110923 - 28 Oct 2022
Cited by 16 | Viewed by 3276
Abstract
In recent decades, the waters off the Antarctic Peninsula and surrounding region have undergone a significant transformation due to global climate change affecting the structure and distribution of pelagic fauna. Here, we present the results of our study on the taxonomic composition and [...] Read more.
In recent decades, the waters off the Antarctic Peninsula and surrounding region have undergone a significant transformation due to global climate change affecting the structure and distribution of pelagic fauna. Here, we present the results of our study on the taxonomic composition and quantitative distribution of plankton communities in Bransfield Strait, Antarctic Sound, the Powell Basin of the Weddell Sea, and the waters off the Antarctic Peninsula and South Orkney Islands during the austral summer of 2022. A slight warming of the Transitional Zonal Water with Weddell Sea influence (TWW) and an increase in its distribution area was detected. Among the pelagic communities, three groups were found to be the most abundant: copepods Calanoides acutus, Metridia gerlachei, and Oithona spp., salpa Salpa thompsoni, and Antarctic krill Euphausia superba. Euphausiids were found in cases of low abundance, species diversity, and biomass. In the studied region, an increase in the amount of the salpa S. thompsoni and the euphausiid Thysanoessa macrura and the expansion of their distribution area were observed. Significant structural shifts in phytoplankton communities manifested themselves in changes in the structure of the Antarctic krill forage base. The composition and distribution of pelagic fauna is affected by a combination of environmental abiotic factors, of which water temperature is the main one. The obtained results have allowed us to assume that a further increase in ocean temperature may lead to a reduction in the number and size of the Antarctic krill population and its successive replacement by salps and other euphausiids that are more resistant to temperature fluctuations and water desalination. Full article
(This article belongs to the Special Issue Marine Biodiversity and Ecosystems Management)
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<p>The sampling stations and the main currents off the Antarctic Peninsula. Numbers represent names of complex stations (except for the station 7303). Dotted lines represent currents, according to [<a href="#B35-diversity-14-00923" class="html-bibr">35</a>,<a href="#B36-diversity-14-00923" class="html-bibr">36</a>]. Orange line—the Antarctic Circumpolar Current (ACC), red line—the Bransfield Current (BC), yellow line—the Antarctic Coastal Current (ACoC), blue line—the Antarctic Shelf Front (ASF), green line—the Weddell Front (WF), grey line—Weddell Deep Water (WDW). Plankton nets: red circle—Double Square Net (DSN) and Multinet integrated, blue circle —DSN, orange diamond—Apshtein, pink triangle—Bongo, green triangle—WP-2.</p>
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<p>Spatial distribution of potential temperature (<b>A</b>) and salinity (<b>B</b>) in the study area at depths of 0–50 m. Stations are marked with black dots; sections considered below are colored in green.</p>
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<p>θ, S-curves of individual stations for each area: orange—the Bransfield Strait, brown—the Antarctic Sound, cyan—the Powell Basin, red—South Orkney Islands shelf. Black boxes: Transitional Zonal Water with Bellingshausen Sea influence (TBW), Transitional Zonal Water with Weddell Sea influence (TWW), modified Circumpolar Deep Water (mCDW), Warm Deep Water (WDW), Shelf Water (SW). The gray lines indicate the potential density at sea surface.</p>
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<p>Vertical distribution of potential temperature (°C, color) and salinity (psu, dotted line) on the sections: (<b>A</b>)—the Bransfield Strait, (<b>B</b>)—the Antarctic Sound, (<b>C</b>)—the Powell Basin and area northeast of the South Orkney Islands. Station numbers are located at the top of the diagram, the geographic location of the station is shown as a vertical line. The bottom topography is taken from the GEBCO2021 database <a href="https://www.gebco.net/data_and_products/gridded_bathymetry_data/gebco_2021/" target="_blank">https://www.gebco.net/data_and_products/gridded_bathymetry_data/gebco_2021/</a> (accessed on 19 May 2022).</p>
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<p>Spatial (<b>A</b>) and vertical ((<b>B</b>), stn. 7336) distribution of chlorophyll <span class="html-italic">a</span> concentrations (mg/m<sup>3</sup>) in the upper 100 m of the water column. Stations are marked with black dots.</p>
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<p>Distribution of floristic groupings of phytoplankton. Groupings: red circle—«Af», blue—«Bf», green—«Cf», pink—«Df». See <a href="#sec2dot4dot1-diversity-14-00923" class="html-sec">Section 2.4.1</a> for grouping abbreviations.</p>
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<p>Distribution of the coenotic groupings of phytoplankton. Groupings: blue circle—«Ac», green—«Bc», violet—«Cc», red—«Dc», orange—«Ec», pink—«Fc». See <a href="#sec2dot4dot1-diversity-14-00923" class="html-sec">Section 2.4.1</a> for grouping abbreviations.</p>
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<p>Total abundance ((<b>A</b>), ind./m<sup>3</sup>), biomass ((<b>B</b>), mg WW/m<sup>3</sup>), and species composition of mesozooplankton in the study area.</p>
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<p>Dendrogram of stations resulting from the cluster analysis (<b>A</b>) based on the abundance of mesozooplankton and the spatial distribution of stations grouped by cluster analysis (<b>B</b>). Groupings: red circle—A, blue—B, pink—C, green—D, black—no grouping. Total abundance (ind./m<sup>3</sup>): 1—100; 2—1200; 3—3000. Diameter of the circle corresponds to the total abundance at a particular station.</p>
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<p>Diagram (3D representation) of the PCO ordination of stations by abundance (ind./1000 m<sup>3</sup>) (<b>A</b>) and spatial distribution of stations grouped by the PCO method (<b>B</b>). Groupings: red circle—main, violet—ice margin, green—S. Orkney shelf. Total abundance (ind./1000 m<sup>3</sup>): 1—50; 2—350; 3—1500; 4—6500. See <a href="#diversity-14-00923-f009" class="html-fig">Figure 9</a> for total abundance designation.</p>
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<p>Spatial distribution of the macrozooplankton abundance (ind./1000 m<sup>3</sup>) and contribution (%) of the most abundant species: blue circle—<span class="html-italic">E. superba</span>, green—<span class="html-italic">T. macrura</span>, red—<span class="html-italic">S. thompsoni</span>. Total abundance (ind./1000 m<sup>3</sup>): 1—0; 2—10; 3—100; 4—400. See <a href="#diversity-14-00923-f009" class="html-fig">Figure 9</a> for total abundance designation.</p>
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<p>Spatial distribution of the macrozooplankton biomass (g/1000 m<sup>3</sup>) and contribution (%) of the most abundant species: blue circle—<span class="html-italic">E. superba</span>, green—<span class="html-italic">T. macrura</span>, red—<span class="html-italic">S. thompsoni</span>. Total biomass (g/1000 m<sup>3</sup>): 1—0; 2—10; 3—100; 4—210. See <a href="#diversity-14-00923-f009" class="html-fig">Figure 9</a> for total abundance designation.</p>
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<p>Dendrogram (<b>A</b>) of the stations resulting from cluster analysis based on the abundance of macrozooplankton and the spatial distribution of stations grouped by cluster analysis (<b>B</b>). Groupings: red circle—A, blue—B. Total abundance (ind./1000 m<sup>3</sup>): 1—0; 2—10; 3—100; 4—400. See <a href="#diversity-14-00923-f009" class="html-fig">Figure 9</a> for total abundance designation.</p>
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<p>Dendrogram of stations resulting from cluster analysis (<b>A</b>), based on the abundance of ichthyoplankton and on the geographical distributions of stations grouped by cluster analysis (<b>B</b>). Groupings: blue circle—A, green—B. Total abundance (ind./m<sup>2</sup>): 1—0; 2—1; 3—5; 4—10. See <a href="#diversity-14-00923-f009" class="html-fig">Figure 9</a> for total abundance designation.</p>
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17 pages, 9723 KiB  
Article
Influence of Hydrological Factors on the Distribution of Methane Fields in the Water Column of the Bransfield Strait: Cruise 87 of the R/V “Academik Mstislav Keldysh”, 7 December 2021–5 April 2022
by Andrei Kholmogorov, Nadezhda Syrbu and Renat Shakirov
Water 2022, 14(20), 3311; https://doi.org/10.3390/w14203311 - 20 Oct 2022
Cited by 3 | Viewed by 1909
Abstract
Within the framework of the expedition research “Complex studies of the Antarctic marine ecosystem in the areas of the transport and interaction of water masses in the Atlantic sector of Antarctica, the Scotia Sea and the Drake Strait” (cruise 87 of the R/V [...] Read more.
Within the framework of the expedition research “Complex studies of the Antarctic marine ecosystem in the areas of the transport and interaction of water masses in the Atlantic sector of Antarctica, the Scotia Sea and the Drake Strait” (cruise 87 of the R/V “Academik Mstislav Keldysh”, 7 December 2021–5 April 2022), the distribution of gas-geochemical fields of methane in the Bransfield Strait was studied in detail for the first time. The connection of the methane distribution in water with the complex hydrological regime of the strait has been revealed. Elevated values of methane concentrations brought to the Bransfield Strait in the warm current flow from the Bellingshausen Sea have been established. Low concentrations of methane also mark the cold waters of the Weddell Sea, which carry out the transit of water masses into the Atlantic Ocean. The research was carried out within the framework of the theme FWMM-2022-033 “Integrated environmental studies of the Southern Ocean” AAAA17-117030110035-4 and international obligations of the Russian Federation as a party to the Antarctic Treaty and the Convention on the Conservation of Antarctic Marine Living Resources. Full article
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<p>A map of sample stations, 87th cruise of R/V “Academik Mstislav Keldysh” (7 December 2021–5 April 2022) and earthquakes for 2020 year according to [<a href="#B27-water-14-03311" class="html-bibr">27</a>].</p>
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<p>Distribution of methane concentration (<b>a</b>), temperature (<b>d</b>) and salinity (<b>b</b>) in the western section (<b>c</b>) in the Bransfield Strait.</p>
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<p>Distribution of methane concentration (<b>a</b>), temperature (<b>d</b>) and salinity (<b>b</b>) in the central section (<b>c</b>) in the Bransfield Strait.</p>
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<p>Distribution of methane concentration (<b>a</b>), temperature (<b>d</b>) and salinity (<b>b</b>) in the eastern section (<b>c</b>) in the Bransfield Strait.</p>
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<p>Diagram of the main components of the Bransfield Strait surface current system [<a href="#B30-water-14-03311" class="html-bibr">30</a>].</p>
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<p><b>Cross-sectional</b> diagram of the main components of the Bransfield Strait current system [<a href="#B30-water-14-03311" class="html-bibr">30</a>].</p>
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<p>Distribution of methane concentration (<b>a</b>), temperature (<b>d</b>) and salinity (<b>b</b>) on a cross section (<b>c</b>) in the Antarctic Sound.</p>
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<p>Distribution of methane concentration (<b>a</b>), temperature (<b>d</b>) and salinity (<b>b</b>) on the longitudinal section (<b>c</b>) in the Antarctic Sound.</p>
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<p>Distribution of methane concentration (<b>a</b>), temperature (<b>d</b>) and salinity (<b>b</b>) on the section (<b>c</b>) in the Weddell Sea south of the Antarctic Sound.</p>
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<p>Distribution of methane concentration (<b>a</b>), temperature (<b>d</b>) and salinity (<b>b</b>) on the 500 km section (<b>c</b>) through the Weddell Sea.</p>
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<p>Methane concentration in the atmosphere.</p>
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