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19 pages, 15754 KiB  
Article
Time Lag Analysis of Atmospheric CO2 and Proxy-Based Climate Stacks on Global–Hemispheric Scales in the Last Deglaciation
by Zhi Liu and Xingxing Liu
Quaternary 2025, 8(1), 11; https://doi.org/10.3390/quat8010011 - 18 Feb 2025
Viewed by 285
Abstract
Based on 88 well-dated and high-resolution paleoclimate records, global and hemispheric stacks of the last deglacial climate were synthesized by utilizing the normalized average method. A sequential relationship between the West Antarctic Ice Sheet Divide ice core CO2 concentration and the composited [...] Read more.
Based on 88 well-dated and high-resolution paleoclimate records, global and hemispheric stacks of the last deglacial climate were synthesized by utilizing the normalized average method. A sequential relationship between the West Antarctic Ice Sheet Divide ice core CO2 concentration and the composited proxy-based global–hemispheric climate stacks was detected using the Wilcoxon rank-sum test and wavelet analysis. The results indicate that the climate stack of the Northern Hemisphere started to increase slowly before 22 kabp, possibly due to the enhancement of summer insolation at high northern latitudes, the onset of warming in the Southern Hemisphere occurred around 19 kabp, and the atmospheric CO2 concentration began to raise around 18.1 kabp. This suggests that the change in northern high-latitude summer insolation was the initial trigger of the last deglaciation, and atmospheric CO2 concentration was an internal feedback associated with global ocean circulation in the Earth’s system. Both the Wilcoxon rank-sum test and wavelet analysis showed that during the BØlling–AllerØd and the Younger Dryas periods there was no obvious asynchrony between the global climate and atmospheric CO2 concentration, which perhaps implies a fast feedback–response mechanism. The seesawing changes in interhemispheric climate and the abrupt variations in the atmospheric CO2 concentration could be explained by the influences of Atlantic meridional overturning circulation strength during the BØlling–AllerØd and the Younger Dryas periods. This reveals that Atlantic meridional overturning circulation played an important role in the course of the last deglaciation. Full article
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Figure 1

Figure 1
<p>The locations and distributions of the 88 included paleoclimatic records.</p>
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<p>The 100-year resolution of global and hemispheric stacks of different data matches. The error bars reflect 1δ standard errors. The symbols (+) and (−) in the legends represent the accepted and abandoned data processing methods, respectively. Therein, area-weighting means to project the participant records onto 5° × 5° geographical coordinates, and interpolation means a 100-year-resolution linear interpolation. LGM: Last glacial maximum. HS1: Heinrich Stadial 1. B-A: BØlling–AllerØd. ACR: Antarctic cold reversal. YD: Younger Dryas. EH: Early Holocene.</p>
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<p>The 100-year-resolution proxy-based global–hemispheric stacks of the last deglacial climate from the normalized average method, where the error bars reflect 1δ standard errors. The olive arrows labeled A–G indicate the seven remarkable transitions. The codes of climate periods are the same as the codes for <a href="#quaternary-08-00011-f002" class="html-fig">Figure 2</a>.</p>
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<p>(<b>a</b>–<b>c</b>) show the 25-year-resolution global, northern, and southern hemispheric stacks with the normalized average method, and the error bars reflect 1δ standard errors. (<b>d</b>) shows the 50-year interpolated WDC CO<sub>2</sub> concentration [<a href="#B17-quaternary-08-00011" class="html-bibr">17</a>]. The smooth lines in (<b>a</b>–<b>d</b>) show the results of quartic polynomial fittings. (<b>e</b>–<b>h</b>) show the <span class="html-italic">p</span> values of the Wilcoxon rank-sum test in different test intervals, where the blue dashed lines indicate the significant confidence level of 95%. (<b>i</b>–<b>l</b>) show the results of the data pretreatments to prepare for the wavelet analysis and the obtained wavelet coefficients, where the light green lines represent the results of detrending and demeaning, the black polylines represent the results of denoising, and the blue lines represent relevant wavelet coefficients.</p>
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<p>Comparisons in detail between atmospheric CO<sub>2</sub> concentration and 100-year-resolution proxy-based global (<b>a</b>), northern (<b>b</b>), and southern (<b>c</b>) hemispheric stacks, as well as the comparison between the Northern and Southern Hemispheres (<b>d</b>). The arrows and vertical purple dashed lines in each subgraph indicate several distinct transitions.</p>
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<p>(<b>a</b>) Summer insolation at 65° N [<a href="#B107-quaternary-08-00011" class="html-bibr">107</a>]. (<b>b</b>) The ice-rafted detritus stack [<a href="#B127-quaternary-08-00011" class="html-bibr">127</a>]. (<b>c</b>) The <sup>231</sup>Pa/<sup>230</sup>Th ratios from marine core OCE326-GGC5, which reflect the strength of the AMOC [<a href="#B114-quaternary-08-00011" class="html-bibr">114</a>]. (<b>d</b>) The 100-year-resolution proxy-based stack of the Northern Hemisphere. (<b>e</b>) The 100-year-resolution proxy-based stack of the Southern Hemisphere. (<b>f</b>) Atmospheric CO<sub>2</sub> concentration [<a href="#B17-quaternary-08-00011" class="html-bibr">17</a>]. The codes of climate periods are the same as the codes for <a href="#quaternary-08-00011-f002" class="html-fig">Figure 2</a>.</p>
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18 pages, 6360 KiB  
Article
Interannual Variability and Trends in Extreme Precipitation in Dronning Maud Land, East Antarctica
by Lejiang Yu, Shiyuan Zhong, Svetlana Jagovkina, Cuijuan Sui and Bo Sun
Remote Sens. 2025, 17(2), 324; https://doi.org/10.3390/rs17020324 - 17 Jan 2025
Viewed by 380
Abstract
This study examines the trends and interannual variability of extreme precipitation in Antarctica, using six decades (1963–2023) of daily precipitation data from Russia’s Novolazarevskaya Station in East Antarctica. The results reveal declining trends in both the annual number of extreme precipitation days and [...] Read more.
This study examines the trends and interannual variability of extreme precipitation in Antarctica, using six decades (1963–2023) of daily precipitation data from Russia’s Novolazarevskaya Station in East Antarctica. The results reveal declining trends in both the annual number of extreme precipitation days and the total amount of extreme precipitation, as well as a decreasing ratio of extreme to total annual precipitation. These trends are linked to changes in northward water vapor flux and enhanced downward atmospheric motion. The synoptic pattern driving extreme precipitation events is characterized by a dipole of negative and positive height anomalies to the west and east of the station, respectively, which directs southward water vapor flux into the region. Interannual variability in extreme precipitation days shows a significant correlation with the Niño 3.4 index during the austral winter semester (May–October). This relationship, weak before 1992, strengthened significantly afterward due to shifting wave patterns induced by tropical Pacific sea surface temperature anomalies. These findings shed light on how large-scale atmospheric circulation and tropical-extratropical teleconnections shape Antarctic precipitation patterns, with potential implications for ice sheet stability and regional climate variability. Full article
(This article belongs to the Special Issue Remote Sensing of Extreme Weather Events: Monitoring and Modeling)
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Graphical abstract

Graphical abstract
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<p>The map of the study region. The asterisk indicates the location of Russia’s Novolazarevskaya Station in East Antarctica.</p>
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<p>Time series of (<b>a</b>) annual precipitation amount (mm) and (<b>b</b>) annual number of days with precipitation (day) for the 1963–2023 period; the 63-yr averaged (<b>c</b>) amount (mm) and (<b>d</b>) number of days (day) of monthly precipitation for the 1963–2023 period; the 63-yr total (<b>e</b>) amount (mm) and (<b>f</b>) number of days (day) of monthly extreme precipitation for the 1963–2023 period.</p>
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<p>Time series of annual (<b>a</b>) number of days and (<b>b</b>) amount of extreme precipitation, SAM index (<b>c</b>), and the ratio of extreme precipitation to the total precipitation (<b>d</b>).</p>
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<p>Composite maps of anomalous 500 hPa geopotential heights (gmp) (<b>a</b>), mean sea level pressure (MSLP) (Pascal) (<b>b</b>), vertically integrated water vapor flux (kg m<sup>−1</sup> s<sup>−1</sup>) (<b>c</b>), and 500 hPa vertical velocity (Pa s<sup>−1</sup>) (<b>d</b>) for extreme precipitation occurrences. Dotted (shaded) regions indicate above 95% confidence level. Red asterisks indicate the location of Russian stations.</p>
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<p>Time series of normalized number of days with extreme precipitation in May-October (black line) and Niño 3.4 index (blue line) (<b>a</b>), the regression map of SST anomalies (°C) (<b>b</b>) and 200 hPa geopotential height anomalies (gmp) (<b>c</b>) onto the normalized number of days with extreme precipitation, the regression map of 200 hPa geopotential height anomalies (gmp) (<b>d</b>) onto the normalized Niño 3.4 index.</p>
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<p>Regression map of anomalous 500 hPa geopotential heights (gmp) (<b>a</b>), mean sea level pressure (Pascal) (<b>b</b>), vertically integrated water vapor flux (kg m<sup>−1</sup> s<sup>−1</sup>) (<b>c</b>), and 500 hPa vertical velocity (Pa s<sup>−1</sup>) (<b>d</b>) onto the normalized number of days with extreme precipitation over the past six decades. Dotted (shaded) regions indicate above 95% confidence level. Red asterisks indicate the location of Russian stations.</p>
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<p>Regression map of anomalous 500 hPa geopotential heights (gmp) (<b>a</b>), mean sea level pressure (Pascal) (<b>b</b>), vertically integrated water vapor flux (kg m<sup>−1</sup> s<sup>−1</sup>) (<b>c</b>), and 500 hPa vertical velocity (Pa s<sup>−1</sup>) (<b>d</b>) onto the normalized Niño 3.4 index over the past six decades. Dotted (shaded) regions indicate above 95% confidence level. Red asterisks indicate the location of Russian stations.</p>
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<p>The moving correlation of the Nino 3.4 index and the number of days with extreme precipitation with the moving windows of 26 (red), 28 (blue), and 30 (black) years (<b>a</b>). The dashed lines indicate <span class="html-italic">p</span> = 0.05 lines. The regression maps of SST anomalies (°C) onto the normalized number of days with extreme precipitation for the 1963–1992 (<b>b</b>) and 1993–2023 (<b>c</b>) periods.</p>
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<p>The regression maps of 200 hPa geopotential height (gmp) anomalies onto the normalized Niño 3.4 index for the 1963–1992 (<b>a</b>) and 1993–2023 (<b>b</b>) periods.</p>
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<p>Regression maps of anomalous 500 hPa geopotential heights (gmp) (<b>a</b>), mean sea level pressure (Pascal) (<b>b</b>), vertically integrated water vapor flux (kg m<sup>−1</sup> s<sup>−1</sup>) (<b>c</b>), and 500 hPa vertical velocity (Pa s<sup>−1</sup>) (<b>d</b>) onto the normalized Niño 3.4 index in May-October for the 1963–1992 period. Dotted (shaded) regions indicate above 95% confidence level. Red asterisks indicate the location of Russian stations.</p>
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<p>Regression maps of anomalous 500 hPa geopotential heights (gmp) (<b>a</b>), mean sea level pressure (Pascal) (<b>b</b>), vertically integrated water vapor flux (kg m<sup>−1</sup> s<sup>−1</sup>) (<b>c</b>), and 500 hPa vertical velocity (Pa s<sup>−1</sup>) (<b>d</b>) onto the normalized Niño 3.4 index in May-October for the 1993–2022 period. Dotted (shaded) regions indicate above 95% confidence level. Red asterisks indicate the location of Russian stations.</p>
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<p>Trends in anomalous 500 hPa geopotential heights (gmp yr<sup>−1</sup>) (<b>a</b>), mean sea level pressure (Pascal yr<sup>−1</sup>) (<b>b</b>), vertically integrated water vapor flux (kg m<sup>−1</sup> s<sup>−1</sup>yr<sup>−1</sup>) (<b>c</b>), and 500 hPa vertical velocity (Pa s<sup>−1</sup> yr<sup>−1</sup>) (<b>d</b>). Dotted (shaded) regions indicate above 95% confidence level. Red asterisks indicate the location of Russian stations.</p>
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44 pages, 7018 KiB  
Review
Rethinking the Lake History of Taylor Valley, Antarctica During the Ross Sea I Glaciation
by Michael S. Stone, Peter T. Doran and Krista F. Myers
Geosciences 2025, 15(1), 9; https://doi.org/10.3390/geosciences15010009 - 4 Jan 2025
Viewed by 556
Abstract
The Ross Sea I glaciation, marked by the northward advance of the Ross Ice Sheet (RIS) in the Ross Sea, east Antarctica, corresponds with the last major expansion of the West Antarctic Ice Sheet during the last glacial period. During its advance, the [...] Read more.
The Ross Sea I glaciation, marked by the northward advance of the Ross Ice Sheet (RIS) in the Ross Sea, east Antarctica, corresponds with the last major expansion of the West Antarctic Ice Sheet during the last glacial period. During its advance, the RIS was grounded along the southern Victoria Land coast, completely blocking the mouths of several of the McMurdo Dry Valleys (MDVs). Several authors have proposed that very large paleolakes, proglacial to the RIS, existed in many of the MDVs. Studies of these large paleolakes have been key in the interpretation of the regional landscape, climate, hydrology, and glacier and ice sheet movements. By far the most studied of these large paleolakes is Glacial Lake Washburn (GLW) in Taylor Valley. Here, we present a comprehensive review of literature related to GLW, focusing on the waters supplying the paleolake, signatures of the paleolake itself, and signatures of past glacial movements that controlled the spatial extent of GLW. We find that while a valley-wide proglacial lake likely did exist in Taylor Valley during the early stages of the Ross Sea I glaciation, during later stages two isolated lakes occupied the eastern and western sections of the valley, confined by an expansion of local alpine glaciers. Lake levels above ~140 m asl were confined to western Taylor Valley, and major lake level changes were likely driven by RIS movements, with climate variables playing a more minor role. These results may have major implications for our understanding of the MDVs and the RIS during the Ross Sea I glaciation. Full article
(This article belongs to the Section Cryosphere)
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Figure 1

Figure 1
<p>(<b>a</b>) Location of Taylor Valley in Antarctica (red dot). (<b>b</b>) Map of Taylor Valley. The blue line shows the extent of the maximum proposed lake level of Glacial Lake Washburn of 336 m asl [<a href="#B4-geosciences-15-00009" class="html-bibr">4</a>]. The red line shows the elevation of the Suess Glacier sill (116 m asl). The Suess Glacier sill location is denoted by the yellow star. The region west of the star is Bonney Basin, and east of the star is lower Taylor Valley (Fryxell Basin and Explorers Cove Basin). The green line shows the threshold elevation of Coral Ridge (78 m asl). The yellow bar shows the location of Coral Ridge. Lakes (lettered) are (a) West Lobe Lake Bonney, (b) East Lobe Lake Bonney, (c) Lake Hoare, (d) Lake Fryxell. The glaciers (numbered) are (1) Taylor Glacier, (2) Rhone Glacier, (3) LaCroix Glacier, (4) Suess Glacier, (5) Canada Glacier, (6) Commonwealth Glacier, (7) Howard Glacier. Dry Valley Drilling Project (DVDP) core sites are marked by red arrows. From east to west those are DVDP 8–10, DVDP 11, and DVDP 12.</p>
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<p>The ‘inverted convection cycle’ formed in MDV proglacial lakes hypothesized by Hendy et al. source [<a href="#B17-geosciences-15-00009" class="html-bibr">17</a>]. Near-freezing surface waters are heated toward 4 °C and descend due to increased density. Meanwhile, along the glacier front, the relatively warm dense bottom waters melt and are cooled by the glacial ice, becoming less dense and ascending toward the surface. As a result, the lake waters undercut the glacier (from source [<a href="#B17-geosciences-15-00009" class="html-bibr">17</a>]).</p>
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<p>Lake Fryxell sediment core log. GSD = Grain Size Distribution (the gravel, sand, silt, and clay fractions are shown from left to right), MS = Magnetic susceptibility, TOC = Total Organic Carbon, TIC = Total Inorganic Carbon, TS = Total Sulphur, and glass refers to volcanic glass associated with the Ross Sea Drift. All ages are estimated based on radiocarbon dates from core materials (modified from source [<a href="#B62-geosciences-15-00009" class="html-bibr">62</a>]).</p>
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<p>Lake Hoare sediment core log. GSD = Grain Size Distribution, TS = Total Sulfur, TOC = Total Organic Carbon, TIC = Total Inorganic Carbon, and glass refers to volcanic glass associated with the Ross Sea Drift (modified from source [<a href="#B67-geosciences-15-00009" class="html-bibr">67</a>]).</p>
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<p>Soil organic matter sources in Taylor Valley. The map shows the transects the soil samples were collected from. Plots show how the isotopic signatures of soil samples from each transect compare with that of marine-derived organic matter (MDOM), lacustrine-derived organic matter (LDOM), and endolith-derived organic matter (EDOM). The numbers associated with points on the plots indicate the elevation in meters that a sample was collected from. BR = Bonney Riegel, MT = Middle Taylor, LH = Lake Hoare, LF = Lake Fryxell, NLT = North Lower Taylor, SLT = South Lower Taylor (modified from source [<a href="#B71-geosciences-15-00009" class="html-bibr">71</a>]).</p>
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<p>Total soluble salt content of Bonney Basin soils (<b>left</b>) and chloride content of Fryxell Basin soils (<b>right</b>) versus elevation. The 300 m asl line in the left plot marks the maximum proposed depth of GLW in Bonney Basin according to Toner et al. source [<a href="#B5-geosciences-15-00009" class="html-bibr">5</a>], and the 116 m asl line indicates the elevation of the Suess Glacier sill. In the right plot, the 78 m and 120 m elevation lines correspond roughly to the threshold elevation of the Coral Ridge and Seuss Glacier sills, respectively. ‘+’ symbols indicate the presence of ice cement at the sample site (modified from source [<a href="#B5-geosciences-15-00009" class="html-bibr">5</a>]).</p>
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<p>A sandy terrace along Huey Creek in Fryxell Basin. Image taken from the creek bed facing north. Photo credit M. Stone.</p>
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<p>Delicate type strandlines (left of the red bracket) next to Rhone Glacier. The red arrows point to the most prominent of these linear features. Photo credit M. Stone.</p>
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<p>An ice push strandline along the shore of modern Lake Fryxell marking the 2014 lake level (highest in the modern record for Lake Fryxell [<a href="#B79-geosciences-15-00009" class="html-bibr">79</a>]). Photo taken in January of 2022 facing east. Photo credit M. Stone.</p>
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<p>Bench type ‘strandlines’ (marked by red arrows) along the south wall of Fryxell Basin. Photo credit M. Stone.</p>
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<p>Resistivity cross section across Lake Fryxell. Vertical dashed lines indicate the surface extent of Lake Fryxell. Note the low resistivity zone, hypothesized to be a remnant talik from an ancient high-level lake, that extends well beyond the limits of modern Lake Fryxell (modified from source [<a href="#B85-geosciences-15-00009" class="html-bibr">85</a>]).</p>
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<p>Distribution of various drift sheets in the McMurdo Dry Valleys. The western part of Taylor Valley is occupied by Bonney Drift (not indicated on map). Note the unmapped drift directly south of Canada Glacier. This region has been mapped as both Bonney Drift (e.g., [<a href="#B84-geosciences-15-00009" class="html-bibr">84</a>]) and Ross Sea Drift (e.g., [<a href="#B6-geosciences-15-00009" class="html-bibr">6</a>]) (modified from source [<a href="#B93-geosciences-15-00009" class="html-bibr">93</a>]).</p>
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<p>Hillshade image showing the moraine ring (indicated by red arrows) off the toe of Canada Glacier (hillshade produced from digital elevation model created by [<a href="#B94-geosciences-15-00009" class="html-bibr">94</a>]).</p>
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<p>Conceptual diagram depicting hypothesized lake ice conveyor processes and deposition (from source [<a href="#B17-geosciences-15-00009" class="html-bibr">17</a>]).</p>
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17 pages, 20240 KiB  
Article
Foundational Aspects for Incorporating Dependencies in Copula-Based Bayesian Networks Using Structured Expert Judgments, Exemplified by the Ice Sheet–Sea Level Rise Elicitation
by Dorota Kurowicka, Willy Aspinall and Roger Cooke
Entropy 2024, 26(11), 949; https://doi.org/10.3390/e26110949 - 5 Nov 2024
Viewed by 784
Abstract
The work presented here marks a further advance in expert uncertainty quantification. In a recent probabilistic evaluation of ice sheet process contributions to sea level rise, tail dependence was elicited and propagated through an uncertainty analysis for the first time. The elicited correlations [...] Read more.
The work presented here marks a further advance in expert uncertainty quantification. In a recent probabilistic evaluation of ice sheet process contributions to sea level rise, tail dependence was elicited and propagated through an uncertainty analysis for the first time. The elicited correlations and tail dependencies concerned pairings of three processes: Accumulation, Discharge and Run-off, which operate on major ice sheets in the West and East Antarctic and in Greenland. The elicitation enumerated dependencies between these processes under selected global temperature change scenarios over different future time horizons. These expert judgments allowed us to populate a Paired Copula Bayesian network model to obtain the estimated contributions of these ice sheets for future sea level rise. Including positive central tendency dependence and tail dependence increases the fatness of the upper tails of projected sea level rise distributions, an amplification important for designing and evaluating possible mitigation strategies. Detailing and jointly computing distributional dependencies and tail dependencies can be crucial components of good practice for assessing the influence of uncertainties on extreme values when modelling stochastic multifactorial processes. Full article
(This article belongs to the Special Issue Bayesian Network Modelling in Data Sparse Environments)
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<p>DAG with 4 nodes.</p>
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<p>Gaussian and Gumbel copula densities both with correlation 0.7.</p>
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<p>Cdf plots of marginal distributions of sea level contributions of West Antarctica due to Discharge assessed by experts 3, 6, 8, 9, 12, 14, 24 and 27, and the performance-based combination PWWD. The latter black dashed line is mainly hidden in the central region of the various expert Cdfs.</p>
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<p>Graph of bivariate relationships assessed by experts.</p>
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<p>PCBN models of all experts with non-zero weights. Types of copulas and conditional copulas (in red) and values of copula parameters are assigned to arks of each graph.</p>
Full article ">Figure 5 Cont.
<p>PCBN models of all experts with non-zero weights. Types of copulas and conditional copulas (in red) and values of copula parameters are assigned to arks of each graph.</p>
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<p>Violin plots of sea level rise distributions obtained with performance-based weights combination with dependence (DepSLR—green) and without (IndSLR—yellow).</p>
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<p>Bar plots of exceedance probabilities for SLR with (green) and without (yellow) dependence.</p>
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<p>Scatter plot matrix of E, G, W and SLR.</p>
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<p>Scatter plot of G and SLR for experts 3 and 14.</p>
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<p>Effect of eliciting dependence and tail dependence of 8 weighted experts. Bars represent 5%, 50% and 95% of SLR distributions of the experts combined with equal (EW) and performance-based (PW) weights in the case of independence (I), Gaussian copulas corresponding to 50% ExcProb assessed by experts (NTD) and with copulas with tail dependence (TD), as discussed in the paper. Experts 3 and 14 with weights 0.28 and 0.3, respectively, are the most important experts.</p>
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<p>Effect of adding eliciting dependence and tail dependence on EW and PW DMs. Bars represent 5%, 50% and 95% of SLR distributions combined with equal (EW) and performance-based (PW) weights in case of independence (I), Gaussian copulas corresponding to 50% ExcProb assessed by experts (NTD) and with copulas with tail dependence (TD), as discussed in the paper.</p>
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12 pages, 1658 KiB  
Article
Two-Step Glaciation of Antarctica: Its Tectonic Origin in Seaway Opening and West Antarctica Uplift
by Hsien-Wang Ou
Glacies 2024, 1(2), 80-91; https://doi.org/10.3390/glacies1020006 - 12 Oct 2024
Cited by 1 | Viewed by 682
Abstract
The Cenozoic glaciation of Antarctica proceeded through two distinct steps around 35 and 15 million years ago. The first icing was attributed to thermal isolation due to the opening of the Drake/Tasman passages and the development of the Antarctic circumpolar current. I also [...] Read more.
The Cenozoic glaciation of Antarctica proceeded through two distinct steps around 35 and 15 million years ago. The first icing was attributed to thermal isolation due to the opening of the Drake/Tasman passages and the development of the Antarctic circumpolar current. I also subscribe to this “thermal isolation” but posit that, although the snowline was lowered below the Antarctic plateau for it to be iced over, the glacial line remains above sea level to confine the ice sheet to the plateau, a “partial” glaciation that would be sustained over time. The origin of the second icing remains unknown, but based on the sedimentary evidence, I posit that it was triggered when the isostatic rebound of West Antarctica caused by heightened erosion rose above the glacial line to be iced over by the expanding plateau ice, and the ensuing cooling lowered the glacial line to sea level to cause the “full” glaciation of Antarctica. To test these hypotheses, I formulate a minimal box model, which is nonetheless subjected to thermodynamic closure that allows a prognosis of the Miocene climate. Applying representative parameter values, the model reproduces the observed two-step icing followed by the stabilized temperature level, in support of the model physics. Full article
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Figure 1
<p>The model configuration showing warm/cold/Antarctic ocean boxes of coupled ocean/atmosphere system. The incoming SW flux (<math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>q</mi> </mrow> <mrow> <mi>i</mi> </mrow> <mrow> <mo>∗</mo> </mrow> </msubsup> </mrow> </semantics></math>), after the atmospheric absorption and reflection by the planetary albedo is absorbed by the ocean (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>q</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math>) to differentiate the SST (thick solid line), which in turn differentiates the SAT by the convective flux <math display="inline"><semantics> <mrow> <mo>(</mo> <msub> <mrow> <mi>q</mi> </mrow> <mrow> <mi>c</mi> </mrow> </msub> </mrow> </semantics></math>). The latitudinal coordinate is defined as <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mrow> <mrow> <mi mathvariant="normal">sin </mi> </mrow> <mo>⁡</mo> <mrow> <mo>(</mo> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">t</mi> <mi mathvariant="normal">i</mi> <mi mathvariant="normal">t</mi> <mi mathvariant="normal">u</mi> <mi mathvariant="normal">d</mi> <mi mathvariant="normal">e</mi> <mo>)</mo> </mrow> </mrow> </mrow> </semantics></math> hence proportional to the surface area on a sphere, and the SST shown is the EOB solution assuming small and thermal-isolated Antarctic box.</p>
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<p>Glacial states for Antarctica: (<b>a</b>) “partial” glaciation when the ice sheet is confined to the Antarctic plateau; (<b>b</b>) “partial-plus” glaciation when the ice sheet covers the Antarctic plateau and West Antarctica; and (<b>c</b>) “full” glaciation when the ice sheet covers entire Antarctica.</p>
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<p>Time evolution of the ice cover subjected to the two icings (circled numbers). <math display="inline"><semantics> <mrow> <mi>P</mi> <mo>/</mo> <msup> <mrow> <mi>P</mi> </mrow> <mrow> <mo>+</mo> </mrow> </msup> <mo>/</mo> <mi>F</mi> <mtext> </mtext> </mrow> </semantics></math> are partial/partial-plus/full glaciation, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>i</mi> </mrow> <mrow> <mi>A</mi> <mi>P</mi> </mrow> </msub> </mrow> </semantics></math>/<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>i</mi> </mrow> <mrow> <mi>W</mi> <mi>A</mi> </mrow> </msub> <mo>/</mo> <msub> <mrow> <mi>i</mi> </mrow> <mrow> <mi>A</mi> </mrow> </msub> </mrow> </semantics></math> are areas of Antarctic plateau/West Antarctica/Antarctica, thin solid/dashed lines are for supercritical/subcritical sensitivity, and thick solid line represents the modeled two-step icing, respectively.</p>
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<p>Modeled time series of SST (thick solid, doubling as summer SAT hence SL), GL (thick dashed), and ice cover <span class="html-italic">i</span> (dotted, in fraction of the cold-box area), with sustained partial/partial+/full glaciations indicated. Thin vertical lines mark the onset of the two icings (circled numbers): the first when SL falls below the Antarctic plateau (light shade) and the second when West Antarctica (dark shade) rises above GL. Transitions between glacial states (striped) are strongly magnified for visualization.</p>
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<p>Range of the effective sensitivity <math display="inline"><semantics> <mrow> <msup> <mrow> <mi>s</mi> </mrow> <mrow> <mo>∗</mo> </mrow> </msup> </mrow> </semantics></math> (shaded) for the observed two-step icing (<math display="inline"><semantics> <mrow> <mi>P</mi> <mo>→</mo> <mi>F</mi> </mrow> </semantics></math>) on the parameter space of global (<math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>s</mi> </mrow> <mo>¯</mo> </mover> </mrow> </semantics></math>) and local (<math display="inline"><semantics> <mrow> <msup> <mrow> <mi>s</mi> </mrow> <mrow> <mo>′</mo> </mrow> </msup> </mrow> </semantics></math>) sensitivities. Above this range, the first icing would fully glaciate Antarctica (<math display="inline"><semantics> <mrow> <mi>F</mi> </mrow> </semantics></math>) to preclude a second icing, and below this range, the second icing only glaciates West Antarctica (<math display="inline"><semantics> <mrow> <mi>P</mi> <mo>→</mo> <msup> <mrow> <mi>P</mi> </mrow> <mrow> <mo>+</mo> </mrow> </msup> </mrow> </semantics></math>). The solid rectangle represents the standard value of sensitivities (<math display="inline"><semantics> <mrow> <mo>±</mo> </mrow> </semantics></math>10%).</p>
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22 pages, 48917 KiB  
Article
Ice Sheet Mass Changes over Antarctica Based on GRACE Data
by Ruiqi Zhang, Min Xu, Tao Che, Wanqin Guo and Xingdong Li
Remote Sens. 2024, 16(20), 3776; https://doi.org/10.3390/rs16203776 - 11 Oct 2024
Viewed by 1115
Abstract
Assessing changes of the mass balance in the Antarctic ice sheet in the context of global warming is a key focus in polar study. This study analyzed the spatiotemporal variation in the Antarctic ice sheet’s mass balance, both as a whole and by [...] Read more.
Assessing changes of the mass balance in the Antarctic ice sheet in the context of global warming is a key focus in polar study. This study analyzed the spatiotemporal variation in the Antarctic ice sheet’s mass balance, both as a whole and by individual basins, from 2003 to 2016 and from 2018 to 2022 using GRACE RL06 data published by the Center for Space Research (CSR) and ERA-5 meteorological data. It explored the lagged relationships between mass balance and precipitation, net surface solar radiation, and temperature, and applied the random forest method to examine the relative contributions of these factors to the ice sheet’s mass balance within a nonlinear framework. The results showed that the mass loss rates of the Antarctic ice sheet during the study periods were −123.3 ± 6.2 Gt/a and −24.8 ± 52.1 Gt/a. The region with the greatest mass loss was the Amundsen Sea in West Antarctica (−488.8 ± 5.3 Gt/a and −447.9 ± 14.7 Gt/a), while Queen Maud Land experienced the most significant mass accumulation (44.9 ± 1.0 Gt/a and 30.0 ± 3.2 Gt/a). The main factors contributing to surface ablation of the Antarctic ice sheet are rising temperatures and increased surface net solar radiation, each showing a lag effect of 1 month and 2 months, respectively. Precipitation also affects the loss of the ice sheet to some extent. Over time, the contribution of precipitation to the changes in the ice sheet’s mass balance increases. Full article
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Figure 1

Figure 1
<p>The Antarctic ice sheet and the sub-basin mapping areas. (<b>a</b>) is the region of the Antarctic ice sheet; (<b>b</b>) is the elevation of the Antarctic ice sheet.</p>
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<p>Flow chart.</p>
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<p>Time series of changes in Antarctic ice sheet mass balance. Time series of mass change in the Antarctic ice sheet, trend significance test <span class="html-italic">p</span> value less than 0.05 is indicated as *, and the change obtained by the 12-month moving average method is shown by the red line.</p>
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<p>Time series of ice sheet mass balance changes in various Antarctic regions. (<b>a</b>) is the West Antarctica and each river basin; (<b>b</b>) is the East Antarctica and each river basin; and (<b>c</b>) is the three Antarctic continents, of which the Antarctic Peninsula and Basin 9 are in the same area. The dotted lines are the ice cover in each basin between 2003–2016 and 201806–2022 fitted trend line.</p>
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<p>Spatial distribution of annual mean equivalent water height of ice sheet mass change at each stage during 2003–2022. (<b>a</b>) 2003–2006; (<b>b</b>) 2007–2008; (<b>c</b>) 2009–2011; (<b>d</b>) 2012–2016; (<b>e</b>) 2019–2022.</p>
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<p>Spatial distribution of annual mean equivalent water height of ice sheet mass change at each stage during 2003–2022. (<b>a</b>) 2003–2006; (<b>b</b>) 2007–2008; (<b>c</b>) 2009–2011; (<b>d</b>) 2012–2016; (<b>e</b>) 2019–2022.</p>
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<p>Spatial distribution of mass balance equivalent water height trend in Antarctic ice sheet (<b>a</b>) 2003–2016; (<b>b</b>) 2018–2022. The shaded areas represent where the trend significance test has <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Time series of monthly and quarterly changes in Antarctic ice sheet mass balance. (<b>a</b>) Multi-year monthly mean of Antarctic ice sheet mass change (blue areas are warm seasons); (<b>b</b>) Time series of ice sheet mass balance changes during the Antarctic warm and cold seasons.</p>
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<p>The spatial distribution and trend of the annual mass balance of the Antarctic ice sheet during the cold and warm seasons from 2003 to 2016.</p>
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<p>The spatial distribution and trend of the annual mass balance of the Antarctic ice sheet during the cold and warm seasons from 2003 to 2016.</p>
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<p>Time series of precipitation, net surface solar radiation, and air temperature in the Antarctic ice sheet region, 2003–2022. The dashed lines are the fitting trend lines for each climate factor during 2003–2022. The blue area represents the accelerated ice sheet loss period (2012–2016), while the red area indicates the dramatic ice sheet loss period (2017–2022).</p>
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<p>The spatial distribution of annual mean and variability trends in precipitation, net surface solar radiation, and air temperature over the Antarctic ice sheet is examined for the period from 2003 to 2022.</p>
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<p>The correlation coefficient between the equivalent water height of the Antarctic ice sheet mass balance and different lag periods of precipitation, net solar radiation, and air temperature on a monthly scale. Points marked with five-pointed stars in the figure have passed the significance test (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Spatial distribution of the number of lag periods of Antarctic ice sheet mass balance on precipitation, surface net solar radiation and temperature change.</p>
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<p>Spatial distribution of the number of lag periods of Antarctic ice sheet mass balance on precipitation, surface net solar radiation and temperature change.</p>
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<p>The relative contribution of climatic factors to the mass balance of the Antarctic ice sheet at the monthly scale. (<b>a</b>) Relative contribution in the same period; (<b>b</b>) Relative contribution of lag 1 period; (<b>c</b>) Relative contribution of lag 2 period. The value represents the proportion of the contribution degree of each influencing factor in that month, and the blue shaded area represents the warm season.</p>
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15 pages, 9622 KiB  
Technical Note
Estimation of Antarctic Ice Sheet Thickness Based on 3D Density Interface Inversion Considering Terrain and Undulating Observation Surface Simultaneously
by Yandong Liu, Jun Wang, Fang Li and Xiaohong Meng
Remote Sens. 2024, 16(11), 1905; https://doi.org/10.3390/rs16111905 - 25 May 2024
Viewed by 1130
Abstract
The thickness of the Antarctic ice sheet is a crucial parameter for inferring glacier mass and its evolution process. In the literature, the gravity method has been proven to be one of the effective means for estimating ice sheet thickness. And it is [...] Read more.
The thickness of the Antarctic ice sheet is a crucial parameter for inferring glacier mass and its evolution process. In the literature, the gravity method has been proven to be one of the effective means for estimating ice sheet thickness. And it is a preferred approach when direct measurements are not available. However, few gravity inversion methods are valid in rugged terrain areas with undulating observation surfaces (UOSs). To solve this problem, this paper proposes an improved high-precision 3D density interface inversion method considering terrain and UOSs simultaneously. The proposed method utilizes airborne gravity data at their flight altitudes, instead of the continued data yield from the unstable downward continuation procedure. In addition, based on the undulating right rectangular prism model, the large reliefs of the terrain are included in the iterative inversion. The proposed method is verified on two synthetic examples and is successfully applied to real data in East Antarctica. Full article
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Figure 1
<p>Interpretation model of an ice sheet, considering terrain and UOSs. The UOSs are drawn with green lines, and the solid black dots represent the observed points. The ice sheet is divided into a series of juxtaposed vertical prisms in blue, and the parameter to be calculated is the elevation of the prisms’ bottom.</p>
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<p>A flow chart of the interface inversion considering terrain and UOSs simultaneously.</p>
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<p>The models and gravity anomaly in synthetic example 1: (<b>a</b>) The elevation of observation surface. (<b>b</b>) The elevation of terrain. (<b>c</b>) The elevation of ice–rock interface. (<b>d</b>) The ice sheet thickness. (<b>e</b>) The theoretical gravity anomaly due to these models.</p>
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<p>The results of the simple models obtained by the conventional method: (<b>a</b>) The calculated gravity anomaly. (<b>b</b>) The residuals of data fitting. (<b>c</b>) The estimated ice thickness. (<b>d</b>) The residuals between the inverted thickness and the true value.</p>
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<p>The results of the simple models obtained by the proposed method: (<b>a</b>) The calculated gravity anomaly. (<b>b</b>) The residuals of data fitting. (<b>c</b>) The estimated ice thickness. (<b>d</b>) The residuals between the inverted thickness and the true value.</p>
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<p>The models and gravity anomaly in synthetic example 2: (<b>a</b>) The elevation of observation surface. (<b>b</b>) The elevation of terrain. (<b>c</b>) The elevation of ice–rock interface. (<b>d</b>) The ice sheet thickness. (<b>e</b>) The theoretical gravity anomaly due to these models.</p>
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<p>The results of the complex models obtained by the conventional method: (<b>a</b>) The calculated gravity anomaly. (<b>b</b>) The residuals of data fitting. (<b>c</b>) The estimated ice thickness. (<b>d</b>) The residuals between the inverted ice thickness and the true value.</p>
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<p>The results of the complex models obtained by the proposed method: (<b>a</b>) The calculated gravity anomaly. (<b>b</b>) The residuals of data fitting. (<b>c</b>) The estimated ice thickness. (<b>d</b>) The residuals between the inverted thickness and the true value.</p>
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<p>Location of the study area in Antarctica. The base map is the elevation of the Antarctic topography. The study area is bounded by a green rectangle.</p>
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<p>The airborne geophysical data from the AGAP Project. Aerogravity data: (<b>a</b>) the residual gravity anomaly caused by the ice–rock interface; (<b>b</b>) the aircraft altitude. Radio-echo sounding data: (<b>c</b>) ice surface elevation, (<b>d</b>) ice bed elevation, and (<b>e</b>) ice thickness.</p>
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<p>The results obtained by the conventional method in the survey area: (<b>a</b>) The calculated gravity anomaly. (<b>b</b>) The residuals of data fitting. (<b>c</b>) The estimated ice thickness. (<b>d</b>) The residuals between the estimated ice thickness and the radar-derived ice thickness.</p>
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<p>The results obtained by the proposed method in the survey area: (<b>a</b>) The calculated gravity anomaly. (<b>b</b>) The residuals of data fitting. (<b>c</b>) The estimated ice thickness. (<b>d</b>) The residuals between the inverted thickness and the radar-derived value.</p>
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16 pages, 5332 KiB  
Review
The Antarctic Subglacial Hydrological Environment and International Drilling Projects: A Review
by Yan Zhou, Xiangbin Cui, Zhenxue Dai, Xiaobing Zhou, Lin Li, Su Jiang and Bo Sun
Water 2024, 16(8), 1111; https://doi.org/10.3390/w16081111 - 13 Apr 2024
Viewed by 1674
Abstract
Subglacial lakes and hydrological systems play crucial roles in Antarctic subglacial hydrology, water balance, subglacial geomorphology, and ice dynamics. Satellite altimetry has revealed that some recurrent water exchange occurs in subglacial lakes. They are referred to as ’active lakes’, which prominently influence a [...] Read more.
Subglacial lakes and hydrological systems play crucial roles in Antarctic subglacial hydrology, water balance, subglacial geomorphology, and ice dynamics. Satellite altimetry has revealed that some recurrent water exchange occurs in subglacial lakes. They are referred to as ’active lakes’, which prominently influence a majority of subglacial hydrological processes. Our analysis indicates that active subglacial lakes are more likely to be situated in regions with higher surface ice flow velocities. Nevertheless, the origin of subglacial lakes still remains enigmatic and uncertain. They could have potential associations with geothermal heat, ice sheets melting, and ice flow dynamics. Subglacial lake drilling and water sampling have the potential to provide valuable insights into the origin of subglacial lakes and subglacial hydrological processes. Moreover, they could also offer unique opportunities for the exploration of subglacial microbiology, evolution of the Antarctic ice sheets, and various fundamental scientific inquiries. To date, successful drilling and sampling has been accomplished in Lake Vostok, Lake Mercer, and Lake Whillans. However, the use of drilling fluids caused the water sample contamination in Lake Vostok, and the drilling attempt at Lake Ellsworth failed due to technical issues. To explore more of the conditions of the Antarctic subglacial lakes, the Lake Centro de Estudios Científicos (Lake CECs) and Lake Snow Eagle (LSE) drilling projects are upcoming and in preparation. In this study, we aim to address the following: (1) introduce various aspects of Antarctic subglacial lakes, subglacial hydrological elements, subglacial hydrology, and the interactions between ice sheets and the ocean; and (2) provide an overview and outlook of subglacial lakes drilling projects. Full article
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<p>An overview diagram of Antarctic subglacial hydrological systems and related detection methods [<a href="#B3-water-16-01111" class="html-bibr">3</a>].</p>
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<p>An overview diagram of Antarctic subglacial lakes and subglacial hydrological systems.</p>
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<p>An illustration depicting ice flow velocities for both stable and active subglacial lakes.</p>
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<p>Comparison of data analysis for stable subglacial lake and active subglacial lake locations in relation to ice flow velocity.</p>
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<p>A diagram of subglacially sourced subglacial channels (modified from Alley et al., 2023 [<a href="#B69-water-16-01111" class="html-bibr">69</a>]).</p>
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<p>A map of the subglacial lake drilling project locations.</p>
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<p>A schematic diagram depicting the LSE parameters (modified from Yan et al., 2022 [<a href="#B51-water-16-01111" class="html-bibr">51</a>]) and the RECAS subglacial access and sampling operations, specifically Steps 1–5: activation of the sonde, drilling downwards, lake sampling, drilling upwards, and arrival to the surface (modified from Talalay et al., 2014 [<a href="#B89-water-16-01111" class="html-bibr">89</a>] and Sun et al., 2023 [<a href="#B93-water-16-01111" class="html-bibr">93</a>]).</p>
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<p>Summary of the scope discussed in this review.</p>
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17 pages, 11670 KiB  
Article
Chronology and Sedimentary Processes in the Western Ross Sea, Antarctica since the Last Glacial Period
by Geng Liu, Zhongshan Shen, Xibin Han, Haifeng Wang, Weiwei Chen, Yi Zhang, Pengyun Ma, Yibing Li, Yun Cai, Pengfei Xue, Huafeng Qin and Chunxia Zhang
J. Mar. Sci. Eng. 2024, 12(2), 254; https://doi.org/10.3390/jmse12020254 - 31 Jan 2024
Viewed by 1216
Abstract
The stability of contemporary ice shelves is under threat due to global warming, and the geological records in the Ross Sea offer such an opportunity to test the linkage between them. However, the absence of calcareous microfossils in the sediments of the Ross [...] Read more.
The stability of contemporary ice shelves is under threat due to global warming, and the geological records in the Ross Sea offer such an opportunity to test the linkage between them. However, the absence of calcareous microfossils in the sediments of the Ross Sea results in uncertainties in establishing a precise chronology for studies. Hence, three sediment cores were collected and studied in terms of radiocarbon dating, magnetic susceptibility, and sediment grain size to reconstruct the environmental processes in the Ross Sea since the last glacial period. The main results are as follows: (1) two grain-size components were identified for the studied cores, which can be correlated to ice-shelf and sea-ice transport, respectively; (2) due to old-carbon contamination and an inconsistent carbon reservoir, the radiocarbon dates were generally underestimated, and as an alternative, changes in magnetic susceptibility of the studied cores can be tuned to the ice-core records to establish a reliable age–depth model and; (3) integrating sediment grain-size changes and comparisons with other paleoenvironmental proxies in the Antarctic, a process from a sub-ice sheet in the last glacial period to a sub-ice shelf in the glacial maximum, and, finally, to a glaciomarine state since the last deglacial period was identified in the western Ross Sea. Integrating these findings, the warming processes in the Antarctic were highlighted in the retreat processes of the Ross Ice Shelf in the past. Full article
(This article belongs to the Section Geological Oceanography)
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<p>Locations of the studied sites, and circulation patterns in the Ross Sea. AASW: Antarctic Surface Water (red arrows); CDW: Circumpolar Deep Water (blue arrows); MCDW: Modified Circumpolar Deep Water (orange arrows); DSW: Dense Shelf Water (green arrows); ISW: Ice Shelf Water (purple arrows). Circulation patterns of the Ross Sea were modified from the work of Smith et al. [<a href="#B35-jmse-12-00254" class="html-bibr">35</a>]. Data of core ANT31-JB06 were from Huang et al. [<a href="#B36-jmse-12-00254" class="html-bibr">36</a>].</p>
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<p>Grain size variation of cores ANT31-JB03, ANT31-JB06, and ANT32-RB16C. The red triangle points represent AMS <sup>14</sup>C dates. Data of core ANT31-JB06 were from Huang et al. [<a href="#B36-jmse-12-00254" class="html-bibr">36</a>].</p>
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<p>Grain-size properties of cores ANT31-JB03, ANT31-JB06, and ANT32-RB16C. (<b>a</b>) Triangle diagram. (<b>b</b>) C–M diagram. (<b>c</b>) Frequency distribution. The solid line represents the average curve and the ribbon is 1σ error. (<b>d</b>–<b>f</b>) PCA results. Data of core ANT31-JB06 were from Huang et al. [<a href="#B36-jmse-12-00254" class="html-bibr">36</a>].</p>
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<p>The age–depth model of the studied cores. Black dots represent calendar dates with 2σ errors. The plot was derived from the Bacon program. Data of core ANT31-JB06 were from Huang et al. [<a href="#B36-jmse-12-00254" class="html-bibr">36</a>].</p>
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<p>Magnetic susceptibility records (χ) of cores ANT31-JB03, ANT31-JB06, and ANT32-RB16C (<b>a</b>–<b>c</b>). EDML nssCa<sup>2+</sup> data (<b>d</b>–<b>f</b>) were from Fischer et al. [<a href="#B53-jmse-12-00254" class="html-bibr">53</a>]. Tuning the magnetic susceptibility records (χ) of cores ANT31-JB03, ANT31-JB06, and ANT32-RB16C to the Antarctic ice-core nssCa<sup>2+</sup> record (<b>g</b>–<b>i</b>). The tuning processes were performed using the QAnalySeries 151 software developed by Kotov et al. [<a href="#B54-jmse-12-00254" class="html-bibr">54</a>]. Data of core ANT31-JB06 were from Huang et al. [<a href="#B36-jmse-12-00254" class="html-bibr">36</a>]. Marine Isotopic Stages (MIS) based on global δ<sup>18</sup>O changes were labeled [<a href="#B55-jmse-12-00254" class="html-bibr">55</a>].</p>
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<p>Scatterplot between various magnetic parameters of cores ANT31-JB03 and ANT32-RB16C.</p>
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<p>Hysteresis loops (calibrated) and IRM acquisition curves of cores ANT31-JB03 (green) and ANT32-RB16C (red).</p>
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<p>FORC diagram for four representative samples of cores ANT31-JB03 and ANT32-RB16C.</p>
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<p>Chronological difference between two age–depth models.</p>
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<p>Comparisons of magnetic susceptibility and sediment grain-size components between the two age–depth models of the studied cores. (<b>a</b>,<b>c</b>) AMS <sup>14</sup>C-based model; (<b>b</b>,<b>d</b>) tuning-based model. The thin lines represent the original values, and the bold lines indicate 1 kyr moving average. Marine Isotopic Stages (MIS) based on global δ<sup>18</sup>O changes were labeled [<a href="#B55-jmse-12-00254" class="html-bibr">55</a>].</p>
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<p>Paleoenvironmental processes recorded in the studied cores and their linkage to Antarctic climate changes. The WDC δ<sup>18</sup>O record (11-point smoothed green curve) [<a href="#B58-jmse-12-00254" class="html-bibr">58</a>]. The stacked anomaly of sea surface temperature in the Southern Ocean (SO SST) [<a href="#B7-jmse-12-00254" class="html-bibr">7</a>]. The Antarctic Cold Reversal (ACR, blue) and Antarctic Isotopic Maxima (AIM 0–2, orange) were labeled [<a href="#B59-jmse-12-00254" class="html-bibr">59</a>]. Data of core ANT31-JB06 were from Huang et al. [<a href="#B36-jmse-12-00254" class="html-bibr">36</a>]. Marine Isotopic Stages (MIS) based on global δ<sup>18</sup>O changes were labeled [<a href="#B55-jmse-12-00254" class="html-bibr">55</a>].</p>
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21 pages, 7150 KiB  
Article
Decoding the Dynamics of Climate Change Impact: Temporal Patterns of Surface Warming and Melting on the Nivlisen Ice Shelf, Dronning Maud Land, East Antarctica
by Geetha Priya Murugesan, Raghavendra Koppuram Ramesh Babu, Mahesh Baineni, Rakshita Chidananda, Dhanush Satish, Sivaranjani Sivalingam, Deva Jefflin Aruldhas, Krishna Venkatesh, Narendra Kumar Muniswamy and Alvarinho Joaozinho Luis
Remote Sens. 2023, 15(24), 5676; https://doi.org/10.3390/rs15245676 - 8 Dec 2023
Cited by 1 | Viewed by 1611
Abstract
This study analyzes the dynamics of surface melting in Antarctica, which are crucial for understanding glacier and ice sheet behavior and monitoring polar climate change. Specifically, we focus on the Nivlisen ice shelf in East Antarctica, examining melt ponds, supra glacial lakes (SGLs), [...] Read more.
This study analyzes the dynamics of surface melting in Antarctica, which are crucial for understanding glacier and ice sheet behavior and monitoring polar climate change. Specifically, we focus on the Nivlisen ice shelf in East Antarctica, examining melt ponds, supra glacial lakes (SGLs), seasonal surface melt extent, and surface ice flow velocity. Spatial and temporal analysis is based on Landsat and Sentinel-1 data from the austral summers of 2000 to 2023. Between 2000 and 2014, melt ponds and SGLs on the ice shelf covered roughly 1 km2. However, from 2015 to 2023, surface melting increased consistently, leading to more extensive melt ponds and SGLs. Significant SGL depths were observed in 2016, 2017, 2019, and 2020, with 2008, 2016, and 2020 showing the highest volumes and progressive SGL area growth. We also examined the relationship between seasonal surface melt extent and ice flow velocity. Validation efforts involved ground truth data from a melt pond in central Dronning Maud Land (cDML) during the 2022–2023 austral summer, along with model-based results. The observed increase in melt pond depth and volume may significantly impact ice shelf stability, potentially accelerating ice flow and ice shelf destabilization. Continuous monitoring is essential for accurately assessing climate change’s ongoing impact on Antarctic ice shelves. Full article
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Graphical abstract

Graphical abstract
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<p>Study area on East Antarctica consisting of Nivlisen Ice Shelf, central Dronning Maud Land, located at 70.3°S, 11.3°E with a 2000 km coastline encompassing large ice shelves with the coordinate reference system “WGS84 Antarctic polar stereographic”.</p>
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<p>Process flow: estimation of surface melt extent (<b>a</b>), depth of melt ponds and supraglacial lakes using melt pond depth model, area and volume (<b>b</b>), and ice flow velocity using (<b>c</b>).</p>
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<p>(<b>a</b>) Schematic representation of the pressure sensor assembly installation over the melt pond situated near Maitri station. (<b>b</b>) The bladder box on the T-junction is fitted at the top end of the hose to allow the logger cable to exit the closed PSA system and the other T-junction’s branch connects to a 2 L rubber expansion bladder.</p>
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<p>Sample of model-based melt pond depth estimates for the austral summer of 2022–2023.</p>
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<p>(<b>a</b>) Maximum depth during austral summers of 2000–2023. (<b>b</b>) Area and volume during austral summers of 2000–2023.</p>
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<p>(<b>a</b>) Maximum depth during austral summers of 2000–2023. (<b>b</b>) Area and volume during austral summers of 2000–2023.</p>
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<p>The surface melt extent (SME) map over NIS for the austral summer 2022–2023.</p>
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<p>The surface melt extent (SME) over NIS for the austral summers 2019–2020, 2020–2021, 2021–2022, and 2022–2023.</p>
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<p>The velocity obtained for 12-day time periods during the months of austral summers considered for the study.</p>
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<p>(<b>a</b>) The unwrapped phase and (<b>b</b>) surface ice flow velocity map obtained during 10 January 2023 (master) and 22 January 2023 (slave) over Dronning Maud Land (negative values indicate movement in the direction away from the satellite along the radar’s line of sight, while positive values indicate movement towards the satellite along the radar’s line of sight).</p>
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<p>The four 30 km long profiles R1, R2, R3, and R4 drawn over NIS in the melt and nonmelt regions (background image is surface ice flow velocity map estimated during January 2023).</p>
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<p>Validation site: glacial lake/melt pond selected near the Maitri, Indian research station located in Dronning Maud Land, East Antarctica.</p>
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<p>(<b>a</b>) An aerial view of the selected melt pond, the RGB image of the lake obtained from P4 multispectral sensor. (<b>b</b>) The view of the lake during the peak melting period, on 20 December 2022. (<b>c</b>) The model-based melt pond depth derived from the UAV survey on 20 December 2022. (<b>d</b>) Correlation analysis of the measured values.</p>
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<p>Location of two ground control points (A and B) near the grounding line of the NIS during the period of 29 December 2022 to 10 January 2023, with a 12-day interval.</p>
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2297 KiB  
Proceeding Paper
Estimation of Air Temperature at Sites in Maritime Antarctica Using MODIS LST Collection 6 Data
by Alejandro Corbea-Pérez, Carmen Recondo and Javier F. Calleja
Environ. Sci. Proc. 2024, 29(1), 34; https://doi.org/10.3390/ECRS2023-15866 - 6 Dec 2023
Viewed by 506
Abstract
It is known that changes in temperature could cause changes in the Antarctic Ice Sheet, which would have an immediate and long-term impact on the global mean sea level. For this reason, the monitoring of air temperature (Ta) is of [...] Read more.
It is known that changes in temperature could cause changes in the Antarctic Ice Sheet, which would have an immediate and long-term impact on the global mean sea level. For this reason, the monitoring of air temperature (Ta) is of great interest to the scientific community. On the other hand, Antarctica constitutes an area of difficult access, which makes it difficult to obtain in situ data. Because of this, Land Surface Temperature (LST) remote sensing data have become an important alternative for estimating Ta. In this work, we estimated Ta from daytime and nighttime LST data at maritime Antarctic sites in the South Shetland Archipelago using empirical models, based on the addition of spatiotemporal variables. We used Ta data from the Spanish Antarctic stations and from the PERMASNOW project stations. MOD11A1 and MYD11A1 (Collection 6) Moderate Resolution Imaging Spectroradiometer (MODIS) LST products were downloaded from the Google Earth Engine platform and only the highest quality data were selected. Outliers associated with clouds were removed with filters. Two different multilinear regression models were tested: models for each individual station and global models based on the data from all the stations. The simple regression analysis LST against Ta showed that a better fit is always achieved with daytime LST data (R2 average = 0.73) than with nighttime LST data (R2 average = 0.56). The performance of the models was improved with the addition of spatiotemporal variables as predictive variables, with which we obtained an average R2 = 0.75 for daytime data and an average R2 = 0.60 for nighttime data. The global models allowed for improving the correlation and reducing the errors with respect to the models obtained using individual stations. Global models provide a precise description of the behavior of the temperature in maritime Antarctica, where it is not possible to install and maintain a dense network of weather stations. Full article
(This article belongs to the Proceedings of ECRS 2023)
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<p>Study area. Left top image: map of Antarctica; in red rectangle, South Shetland Islands (SSI) archipelago. Left bottom image: Livingston Island; in red rectangle, Hurd Peninsula, where the stations used in this work are located. Right image: location of the stations.</p>
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<p>Linear correlation between JCI mean daily air temperature, and MOD11A1 daytime LST (on the <b>left</b>) and MYD11A1 nighttime LST (on the <b>right</b>).</p>
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20 pages, 9365 KiB  
Article
Airborne Radio-Echo Sounding Data Denoising Using Particle Swarm Optimization and Multivariate Variational Mode Decomposition
by Yuhan Chen, Sixin Liu, Kun Luo, Lijuan Wang and Xueyuan Tang
Remote Sens. 2023, 15(20), 5041; https://doi.org/10.3390/rs15205041 - 20 Oct 2023
Cited by 2 | Viewed by 1378
Abstract
Radio-echo sounding (RES) is widely used for polar ice sheet detection due to its wide coverage and high efficiency. The multivariate variational mode decomposition (MVMD) algorithm for the processing of RES data is an improvement to the variational mode decomposition (VMD) algorithm. It [...] Read more.
Radio-echo sounding (RES) is widely used for polar ice sheet detection due to its wide coverage and high efficiency. The multivariate variational mode decomposition (MVMD) algorithm for the processing of RES data is an improvement to the variational mode decomposition (VMD) algorithm. It processes data encompassing multiple channels. Determining the most effective component combination of the penalty parameter (α) and the number of intrinsic mode functions (IMFs) (K) is fundamental and affects the decomposition results. α and K in traditional MVMD are provided by subjective experience. We integrated the particle swarm optimization (PSO) algorithm to iteratively optimize these parameters—specifically, α and K—with high precision. This was then combined with the four quantitative parameters: energy entropy, signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), and root-mean-square error (RMSE). The RES signal decomposition results were judged, and the most effective component combination for noise suppression was selected. We processed the airborne RES data from the East Antarctic ice sheet using the combined PSO–MVMD method. The results confirmed the quality of the proposed method in attenuating the RES signal noise, enhancing the weak signal of the ice base, and improving the SNR. This combined PSO–MVMD method may help to enhance weak signals in deeper parts of ice sheets and may be an effective tool for RES data interpretation. Full article
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<p>Dual-channel composite input signal.</p>
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<p>Decomposed IMFs using VMD for the dual-channel composite input signal ((<b>a</b>–<b>c</b>): waveform diagrams; (<b>d</b>–<b>f</b>): spectrum diagrams).</p>
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<p>Decomposed IMFs using MVMD for the dual-channel composite input signal ((<b>a</b>–<b>c</b>): waveform diagrams; (<b>d</b>–<b>f</b>): spectrum diagrams).</p>
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<p>Flowchart of PSO algorithm searching for <math display="inline"><semantics> <mrow> <mfenced open="[" close="]" separators="|"> <mrow> <mi>k</mi> <mo>,</mo> <mi>α</mi> </mrow> </mfenced> </mrow> </semantics></math>.</p>
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<p>(<b>a</b>) Composite input signal and its component signals; (<b>b</b>) spectrum of the composite input signal.</p>
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<p>(<b>a</b>) Initial position of particles for component signals; (<b>b</b>) local minimal entropy value with PSO evolution; (<b>c</b>) final position of the particle.</p>
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<p>Decomposed IMFs using MVMD for the composite input signal ((<b>a</b>–<b>i</b>): waveform diagrams; (<b>j</b>–<b>r</b>): spectrum diagrams).</p>
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<p>Location of the survey line AB in the Antarctic (background image is the Bedmap2 elevation).</p>
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<p>Raw data profile in <a href="#remotesensing-15-05041-f008" class="html-fig">Figure 8</a>.</p>
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<p>Flowchart of data-processing procedure.</p>
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<p>(<b>a</b>) Initial position of particles for RES data; (<b>b</b>) local minimal entropy value with PSO evolution; (<b>c</b>) final position of the particle.</p>
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<p>IMFs after MVMD decomposition.</p>
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<p>MVMD reconstruction profile.</p>
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<p>(<b>a</b>) IMF3 + IMF6 + IMF7 combination profile; (<b>b</b>) IMF1 + IMF2 + IMF4 combination profile.</p>
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<p>(<b>a</b>) Raw data detail profile; (<b>b</b>) MVMD reconstruction detail profile.</p>
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<p>Decomposition of RES data into 7 IMF runtime comparisons: (<b>a</b>) MVMD runtime; (<b>b</b>) VMD runtime.</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 1491
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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<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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26 pages, 8153 KiB  
Article
Geophysics in Antarctic Research: A Bibliometric Analysis
by Yuanyuan Zhang, Changchun Zou, Cheng Peng, Xixi Lan and Hongjie Zhang
Remote Sens. 2023, 15(16), 3928; https://doi.org/10.3390/rs15163928 - 8 Aug 2023
Cited by 1 | Viewed by 2855
Abstract
Antarctica is of great importance in terms of global warming, the sustainability of resources, and the conservation of biodiversity. However, due to 99.66% of the continent being covered in ice and snow, geological research and geoscientific study in Antarctica face huge challenges. Geophysical [...] Read more.
Antarctica is of great importance in terms of global warming, the sustainability of resources, and the conservation of biodiversity. However, due to 99.66% of the continent being covered in ice and snow, geological research and geoscientific study in Antarctica face huge challenges. Geophysical surveys play a crucial role in enhancing comprehension of the fundamental structure of Antarctica. This study used bibliometric analysis to analyze citation data retrieved from the Web of Science for the period from 1982 to 2022 with geophysical research on Antarctica as the topic. According to the analysis results, the amount of Antarctic geophysical research has been steadily growing over the past four decades as related research countries/regions have become increasingly invested in issues pertaining to global warming and sustainability, and international cooperation is in sight. Moreover, based on keyword clustering and an analysis of highly cited papers, six popular research topics have been identified: Antarctic ice sheet instability and sea level change, Southern Ocean and Sea Ice, tectonic activity of the West Antarctic rift system, the paleocontinental rift and reorganization, magmatism and volcanism, and subglacial lakes and subglacial hydrology. This paper provides a detailed overview of these popular research topics and discusses the applications and advantages of the geophysical methods used in each field. Finally, based on keywords regarding abrupt changes, we identify and examine the thematic evolution of the nexus over three consecutive sub-periods (i.e., 1990–1995, 1996–2005, and 2006–2022). The relevance of using geophysics to support numerous and diverse scientific activities in Antarctica becomes very clear after analyzing this set of scientific publications, as is the importance of using multiple geophysical methods (satellite, airborne, surface, and borehole technology) to revolutionize the acquisition of new data in greater detail from inaccessible or hard-to-reach areas. Many of the advances that they have enabled be seen in the Antarctic terrestrial areas (detailed mapping of the geological structures of West and East Antarctica), ice, and snow (tracking glaciers and sea ice, along with the depth and features of ice sheets). These valuable results help identify potential future research opportunities in the field of Antarctic geophysical research and aid academic professionals in keeping up with recent advances. Full article
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<p>Bibliometric lines of research for Phases 1 (light blue) and 2 (dark blue).</p>
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<p>Time evolution of annual publications and the accumulated literature from 1982 to 2022.</p>
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<p>Distribution of the top 15 countries with publications in Antarctic geophysical research.</p>
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<p>Distribution of major countries/institutions for Antarctic geophysical research.</p>
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<p>The cooperation network of the most productive countries, in terms of Antarctic geophysical research.</p>
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<p>Geophysical observation methods applied to the Antarctic ice sheet (modified from [<a href="#B118-remotesensing-15-03928" class="html-bibr">118</a>]).</p>
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<p>(<b>a</b>) The minimum daily sea ice extent (SIE) (10<sup>6</sup> km<sup>2</sup>) for the Southern Ocean along with the daily maximum and annual mean for 1979–2022 [<a href="#B147-remotesensing-15-03928" class="html-bibr">147</a>]; (<b>b</b>) Antarctic sea ice drift velocity trend during 1992–2015 [<a href="#B150-remotesensing-15-03928" class="html-bibr">150</a>]; (<b>c</b>) Maps of the average sea ice thickness data for each season from 2003 to 2008 [<a href="#B142-remotesensing-15-03928" class="html-bibr">142</a>].</p>
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<p>(<b>a</b>) Main geophysical methods for detecting subglacial hydrology [<a href="#B206-remotesensing-15-03928" class="html-bibr">206</a>]; (<b>b</b>) ICESat-2 altimetry coverage of active subglacial lakes in Antarctica [<a href="#B213-remotesensing-15-03928" class="html-bibr">213</a>]; (<b>c</b>) Radargrams of the Antarctic subglacial hydrological environment [<a href="#B214-remotesensing-15-03928" class="html-bibr">214</a>]; (<b>d</b>) The subglacial lake was imaged using seismic data [<a href="#B215-remotesensing-15-03928" class="html-bibr">215</a>].</p>
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<p>(<b>a</b>) Antarctic Inventory of subglacial lakes, Red circles represent stable lakes and blue triangles represent active lakes [<a href="#B206-remotesensing-15-03928" class="html-bibr">206</a>]; (<b>b</b>) subglacial water system of the Siple Coast in West Antarctica Major water-flow pathways connect subglacial lakes (in black) within a large-scale distributed system of subglacial till layers and linked cavities [<a href="#B107-remotesensing-15-03928" class="html-bibr">107</a>].</p>
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19 pages, 17464 KiB  
Article
The Antarctic Amplification Based on MODIS Land Surface Temperature and ERA5
by Aihong Xie, Jiangping Zhu, Xiang Qin and Shimeng Wang
Remote Sens. 2023, 15(14), 3540; https://doi.org/10.3390/rs15143540 - 14 Jul 2023
Cited by 5 | Viewed by 1994
Abstract
With global warming accelerating, polar amplification is one of the hot issues in climate research. However, most studies focus on Arctic amplification, and little attention has been paid to Antarctic amplification (AnA), and there is no relevant research based on MODIS (Moderate Resolution [...] Read more.
With global warming accelerating, polar amplification is one of the hot issues in climate research. However, most studies focus on Arctic amplification, and little attention has been paid to Antarctic amplification (AnA), and there is no relevant research based on MODIS (Moderate Resolution Imaging Spectroradiometer) land surface temperature observations. Compared with 128 stations’ observations, MODIS can capture the variations in temperature over Antarctica. In addition, the temperature changes in Antarctica, East Antarctica, West Antarctica and the Antarctic Peninsula during the period 2001–2018 reflected by the MODIS and ERA5 are basically consistent, and the temperature changes in Antarctica are negatively correlated with the Southern Annular Mode. AnA occurs under all annual and seasonal scales, with an AnA index greater than 1.27 (1.31) from the MODIS (ERA5), and is strongest in the austral winter and weakest in summer. AnA displays regional differences, and the signal from the MODIS is similar to that from ERA5. The strongest amplification occurs in East Antarctica, with an AnA index greater than 1.45 (1.48) from the MODIS (ERA5), followed by West Antarctica, whereas the amplified signal is absent at the Antarctic Peninsula. In addition, seasonal differences can be observed in the sub regions of Antarctica. For West Antarctica, the greatest amplification appears in austral winter, and in austral spring for East Antarctica. The AnA signal also can be captured in daytime and nighttime observations, and the AnA in nighttime observations is stronger than that in daytime. Generally, the MODIS illustrates the appearance of AnA for the period 2001–2018, and the Antarctic climate undergoes drastic changes, and the potential impact should arouse attention. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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<p>The location and topographic map of the Antarctic Ice Sheet.</p>
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<p>Spatial distribution of 128 stations cited in the text. Note that the numbers refer to the lists in <a href="#app1-remotesensing-15-03540" class="html-app">Table S1</a>.</p>
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<p>Correlation coefficients and bias of monthly surface temperature simulation for 128 stations from MODIS observations and ERA5. Note: the correlation coefficients are all significant at the 95% confidence interval.</p>
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<p>Comparisons between station time series and MODIS at four selected stations located in the East Antarctica coast, the interior of East Antarctica, West Antarctica and the Antarctic Peninsula.</p>
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<p>The mean annual and seasonal temperature anomalies for surface temperature based on ERA5 (solid line) and MODIS land surface temperatures (dotted line), spatially averaged over Antarctica, and the sub regions of East Antarctica, West Antarctica and the Antarctic Peninsula, and the Southern Hemisphere, respectively. The seasonal scale is for austral seasons of spring (SON, September–November), summer (DJF, December–February), autumn (MAM, March–May), and winter (JJA, June–August), respectively.</p>
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<p>The distribution of temperature trend (°C per decade) over Antarctica for annual, austral spring (SON, September–November), summer (DJF, December–February), autumn (MAM, March–May) and winter (JJA, June–August) mean from ERA5 (<b>left</b> panel) and MODIS land surface temperatures (<b>right</b> panel), respectively. The gray shaded areas with trends significant at the 95% confidence level.</p>
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<p>The distribution of correlation coefficients between the ERA5 and MODIS temperatures over Antarctica for annual, austral spring (SON, September–November), summer (DJF, December–February), autumn (MAM, March–May) and winter (JJA, June–August). The gray stippling shows the regions that fail to pass the 95% significant confidence test.</p>
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<p>The correlation coefficients in the mean SAM indices and the corresponding near-surface temperatures over the Antarctic Ice Sheet (AIS), East Antarctic Ice Sheet (EAIS), West Antarctic Ice Sheet (WAIS) and Antarctic Peninsula (AP), for annual, austral spring (SON, September–November), summer (DJF, December–February), autumn (MAM, March–May) and winter (JJA, June–August) mean from ERA5 and MODIS observations. All the correlation coefficients are significant at the 95% confidence level.</p>
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<p>The distribution of annual correlation coefficients between the following variables—surface downward longwave radiation (sdlr), total column ozone (tco), specific-humidity at 850 hPa (q), total cloud cover (tcc) and cloud base height (cbh)—and the temperature over Antarctica. The gray stippling shows the regions that fail to pass the 95% significant confidence test.</p>
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<p>Spatial patterns of amplification index over Antarctica for annual, austral spring (SON, September–November), summer (DJF, December–February), autumn (MAM, March–May) and winter (JJA, June–August) mean from ERA5 and the MODIS, and the amplification index from 128 stations compared with ERA5 and the MODIS, respectively.</p>
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<p>Annual Antarctic amplification index with differing start years and interval lengths from ERA5 and MODIS observations.</p>
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<p>Surface air temperature including ocean and land regions, and for land only (°C per decade) trends of zonal average in different latitudes based on ERA5 during 2001–2018 in the Southern Hemisphere for annual and austral spring (SON, September–November), summer (DJF, December–February), autumn (MAM, March–May) and winter (JJA, June–August), respectively.</p>
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<p>Similar to <a href="#remotesensing-15-03540-f010" class="html-fig">Figure 10</a>, but for the amplification index in ERA5 based on the Southern Hemisphere including both ocean and land regions, and the amplification index from 128 stations compared with ERA5.</p>
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