High-resolution mapping of circum-Antarctic landfast sea ice distribution, 2000–2018

Fraser, Alexander; Massom, Robert A.; Ohshima, Κay I.; Willmes, Sascha; Kappes, Peter J.; Cartwright, Jessica; Porter-Smith, Richard

doi:10.5194/essd-12-2987-2020

Cited by 44 publications

(72 citation statements)

References 29 publications

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“…Satellite remote sensing has been instrumental to understand recent change, largescale dynamics, and processes relevant to sea ice prediction, including passive microwave (e.g., special sensor microwave imager [1]), optical (e.g., moderate resolution imaging spectroradiometer [11]), and altimetry (e.g., ice cloud, and land elevation satellite-2 [12]) systems. Synthetic aperture radar (SAR) missions (e.g., ERS, RADARSAT, Sentinel) have been crucial for providing insight into sea ice properties, e.g., [13], dynamics, e.g., [14], and change relevant for sea ice use and regional assessments [9,15,16].…”

Section: Introductionmentioning

confidence: 99%

Ground-Based Radar Interferometry of Sea Ice

et al. 2020

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In light of recent Arctic change, there is a need to better understand sea ice dynamic processes at the floe scale to evaluate sea ice stability, deformation, and fracturing. This work investigates the use of the Gamma portable radar interferometer (GPRI) to characterize sea ice displacement and surface topography. We find that the GPRI is best suited to derive lateral surface deformation due to mm-scale horizontal accuracy. We model interferometric phase signatures from sea ice displacement and evaluate possible errors related to noise and antenna motion. We compare the analysis with observations acquired during a drifting ice camp in the Beaufort Sea. We used repeat-scan and stare-mode interferometry to identify two-dimensional shear and to track continuous uni-directional convergence. This paper demonstrates the capacity of the GPRI to derive surface strain on the order of 10−7 and identify different dynamic regions based on sub-mm changes in displacement. The GPRI is thus a promising tool for sea ice applications due to its high accuracy that can potentially resolve pre- and post-fracture deformation relevant to sea ice stability and modeling.

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Section: Introductionmentioning

confidence: 99%

Ground-Based Radar Interferometry of Sea Ice

et al. 2020

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“…3 Difference in the surface forcing in the experiments with and without fast-ice cover Fast ice is formed by sea ice fastening to the Antarctic coastline and/or ice shelf front, and the typical thickness is a few meters (Fraser et al, 2012;Giles et al, 2008). Note again that the fast ice in this model is represented as an uppermost onegrid ice shelf.…”

Section: Atmospheric Conditionsmentioning

confidence: 99%

Modeling intensive ocean–cryosphere interactions in Lützow-Holm Bay, East Antarctica

et al. 2021

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Abstract. Basal melting of Antarctic ice shelves accounts for more than half of the mass loss from the Antarctic ice sheet. Many studies have focused on active basal melting at ice shelves in the Amundsen–Bellingshausen seas and the Totten ice shelf, East Antarctica. In these regions, the intrusion of Circumpolar Deep Water (CDW) onto the continental shelf is a key component for the localized intensive basal melting. Both regions have a common oceanographic feature: southward deflection of the Antarctic Circumpolar Current brings CDW toward the continental shelves. The physical setting of the Shirase Glacier tongue (SGT) in Lützow-Holm Bay corresponds to a similar configuration on the southeastern side of the Weddell Gyre in the Atlantic sector. Here, we conduct a 2–3 km resolution simulation of an ocean–sea ice–ice shelf model using a recently compiled bottom-topography dataset in the bay. The model can reproduce the observed CDW intrusion along the deep trough. The modeled SGT basal melting reaches a peak in summer and a minimum in autumn and winter, consistent with the wind-driven seasonality of the CDW thickness in the bay. The model results suggest the existence of an eastward-flowing undercurrent on the upper continental slope in summer, and the undercurrent contributes to the seasonal-to-interannual variability in the warm water intrusion into the bay. Furthermore, numerical experiments with and without fast-ice cover in the bay demonstrate that fast ice plays a role as an effective thermal insulator and reduces local sea ice formation, resulting in much warmer water intrusion into the SGT cavity.

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“…around the circumpolar Antarctic margin. Localized case studies have shown coastline geometry and aspect to be a major determinant of the distribution and properties of sea ice in the Antarctic coastal zone (Fraser et al, 2012;Giles et al, 2008;Massom et al, 2001) and to affect important ice sheet margin processes, e.g. ice shelf-ocean interaction, melt, and iceberg calving, with implications for sea level rise.…”

Section: Introductionmentioning

confidence: 99%

Coastal complexity of the Antarctic continent

Porter-Smith

McKinlay

Fraser

et al. 2021

Earth Syst. Sci. Data

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Abstract. The Antarctic outer coastal margin (i.e. the coastline itself or the terminus or front of ice shelves, whichever is adjacent to the ocean) is a key interface between the ice sheet and terrestrial environments and the Southern Ocean. Its physical configuration (including both length scale of variation, orientation, and aspect) has direct bearing on several closely associated cryospheric, biological, oceanographical, and ecological processes, yet no study has quantified the coastal complexity or orientation of Antarctica's coastal margin. This first-of-a-kind characterization of Antarctic coastal complexity aims to address this knowledge gap. We quantify and investigate the physical configuration and complexity of Antarctica's circumpolar outer coastal margin using a novel technique based on ∼ 40 000 random points selected along a vector coastline derived from the MODIS Mosaic of Antarctica dataset. At each point, a complexity metric is calculated at length scales from 1 to 256 km, giving a multiscale estimate of the magnitude and direction of undulation or complexity at each point location along the entire coastline. Using a cluster analysis to determine characteristic complexity “signatures” for random nodes, the coastline is found to comprise three basic groups or classes: (i) low complexity at all scales, (ii) most complexity at shorter scales, and (iii) most complexity at longer scales. These classes are somewhat heterogeneously distributed throughout the continent. We also consider bays and peninsulas separately and characterize their multiscale orientation. This unique dataset and its summary analysis have numerous applications for both geophysical and biological studies. All these data are referenced by https://doi.org/10.26179/5d1af0ba45c03 (Porter-Smith et al., 2019) and are available free of charge at http://data.antarctica.gov.au (last access: 7 June 2021).

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High-resolution mapping of circum-Antarctic landfast sea ice distribution, 2000–2018

Cited by 44 publications

References 29 publications

Ground-Based Radar Interferometry of Sea Ice

Ground-Based Radar Interferometry of Sea Ice

Modeling intensive ocean–cryosphere interactions in Lützow-Holm Bay, East Antarctica

Coastal complexity of the Antarctic continent

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