Modes of Antarctic tidal grounding line migration revealed by Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) laser altimetry

Tide-forced short-term migration of the grounding line (GL) of Antarctic ice shelves can impact ice dynamics at the ice sheet margins and obscures assessments of long-term GL advance or retreat. However, the magnitude of tidally induced GL migration is poorly known, and the spatial patterns and modes of variability are not well characterised. Here we develop and apply a technique that uses Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) repeat-track laser altimetry to locate the inland limit of tidal ice shelf flexure for each sampled tide, enabling the magnitude and temporal variability of tidal GL migration to be resolved. We demonstrate its application at an ice plain north of Bungenstockrücken, in a region of the southern Ronne Ice Shelf subject to large ocean tides. We observe a 1300 km2 area of ephemeral grounding over which the GL migrates by up to 15 km between low and high tide and identify four distinct modes of migration: linear, asymmetric, threshold and hysteresis. The short-term movement of the GL dominates any long-term migration signal in this location, and the distribution of GL positions and modes contains information about spatial variability in the ice–bed interface. We discuss the impact of extreme tidal GL migration on ice shelf–ocean–subglacial systems in Antarctica and make recommendations for how GLs should be more precisely defined and documented in future by the community.</p

Open access data in polar and cryospheric remote sensing

This paper aims to introduce the main types and sources of remotely sensed data that are freely available and have cryospheric applications. We describe aerial and satellite photography, satellite-borne visible, near-infrared and thermal infrared sensors, synthetic aperture radar, passive microwave imagers and active microwave scatterometers. We consider the availability and practical utility of archival data, dating back in some cases to the 1920s for aerial photography and the 1960s for satellite imagery, the data that are being collected today and the prospects for future data collection; in all cases, with a focus on data that are openly accessible. Derived data products are increasingly available, and we give examples of such products of particular value in polar and cryospheric research. We also discuss the availability and applicability of free and, where possible, open-source software tools for reading and processing remotely sensed data. The paper concludes with a discussion of open data access within polar and cryospheric sciences, considering trends in data discoverability, access, sharing and use.A. Pope would like to acknowledge support from the Earth Observation Technology Cluster, a knowledge exchange project, funded by the Natural Environment Research Council (NERC) under its Technology Clusters Programme, the U.S. National Science Foundation Graduate Research Fellowship Program, Trinity College (Cambridge) and the Dartmouth Visiting Young Scientist program sponsored by the NASA New Hampshire Space Grant.This is the final published version. It's also available from MDPI at http://www.mdpi.com/2072-4292/6/7/6183

The recent dynamics of Moscow University Glacier and Moscow University Ice Shelf, East Antarctica (1963 – 2022)

Mass loss from the Antarctic Ice Sheet is dominated by ice discharge through outlet glaciers, many of which are buttressed by peripheral ice shelves. In Wilkes Land, East Antarctica, an ocean-driven increase in ice flux from several large outlet glaciers has caused accelerated mass loss over recent decades. Wilkes Land overlies the Aurora Subglacial Basin (ASB), which contains an ice volume large enough to raise global sea level by 5 m and is potentially susceptible to pervasive retreat. However, ice dynamics within some areas of Wilkes Land remain largely unstudied. This includes Moscow University Glacier (MUG) and Moscow University Ice Shelf (MUIS), which regulate ice discharge from a catchment containing 128 cm of potential sea level rise within the ASB and are subject to intrusions of warm Circumpolar Deep Water. Employing optical satellite imagery and remote sensing datasets to record changes in terminus position, ice surface velocity, ice surface elevation, grounding line location and sea ice distribution, this thesis aims to investigate the ice dynamics of MUIS and MUG between 1963 and 2022. Migration of the MUIS ice front is limited to the unconfined ice shelf region. Both MUG and MUIS exhibited negligible change in flow velocity between 2000 and 2021, and the results suggest limited grounding line retreat of 1.4 km between 1996 and 2017 (~67 m yr). Ice surface elevation remained stable from 1993 to 2010, but MUG was recorded to thin at an accelerated rate (0.86 m yr) between 2011 and 2016, and regions of enhanced surface lowering were observed to correlate with areas of faster ice flow. Overall, these findings imply that MUG and MUIS have remained largely stable in recent decades, but may be starting to exhibit the early indicators of dynamic change. It is suggested that topography exerts critical stabilising stresses on MUIS, enhancing its capacity to buttress the flow of MUG. Continued monitoring of MUG and MUIS, as well as topographically-constrained ice flow modelling, will be important in understanding the response of the Moscow University catchment to future ocean forcing

Mapping the grounding line of Antarctica in SAR interferograms with machine learning techniques

The grounding line marks the transition between ice grounded at the bedrock and the floating ice shelf. Its location is required for estimating ice sheet mass balance, modelling of ice sheet dynamics and glaciers and for evaluating ice shelf stability, which merits its long-term monitoring. The line migrates both due to short term influences such as ocean tides and atmospheric pressure, and long-term effects such as changes of ice thickness, slope of bedrock and variations in sea level. Of the numerous in-situ and remote sensing methods currently in use to map the grounding line, Differential Interferometric Synthetic Aperture Radar (DInSAR) is, by far, the most accurate technique which produces spatially dense delineations. Tidal deformation at the ice sheet-ice shelf boundary is visible as a dense fringe belt in DInSAR interferograms and its landward limit is taken as a good approximation of the grounding line location (GLL). The GLL is usually manually digitized on the interferograms by human operators. This is both time consuming and introduces inconsistencies due to subjective interpretation especially in low coherence interferograms. On a large scale and with increasing data availability a key challenge is the automation of the delineation procedure. So far, a limited amount of studies were published regarding the delineation processes of typical features on the ice sheets using deep neural networks (DNNs). The objectives of this thesis were to further explore the feasibility of using machine learning for mapping the interferometric grounding line, as well as exploring the contributions of complementary features such as coherence estimated from phase, Digital Elevation Model, ice velocity, tidal displacement and atmospheric pressure, in addition to DInSAR interferograms. A dataset composed of manually delineated GLLs generated within ESA’s Antarctic Ice Sheet Climate Change Initiative project and corresponding DInSAR interferograms from ERS-1/2, Sentinel-1 and TerraSAR-X missions over Antarctica together with the above mentioned features was compiled and used for training two DNNs: Holistically-Nested Edge Detection (HED) andUNet. The developed processing chain handles creation of the training feature stack, training the DNNs and performing post processing functions on the resulting predictions. HED outperformed UNet and was able to achieve a median deviation (from manual delineations) of 209.23 m with a median absolute deviation of 152.91 m. Analysis of the individual feature contributions revealed that only the phase and derived features (real and imaginary interferogram components and coherence estimates) substantially influence the predicted GLLs. This finding is advantageous in terms of saving time, computational effort and memory in creating and storing the above mentioned feature stack. Although the delineations generated from HED do not perfectly follow the true GLL in all locations, the gains in efficiency and consistency are considerable, compared to the time and effort spent for manual digitizations. This study shows the potential of DNNs for automating the interferometric GLL delineation process

Mapping the grounding line of Antarctica in SAR interferograms with machine learning techniques

The grounding line marks the transition between ice grounded at the bedrock and the floating ice shelf. Its location is required for estimating ice sheet mass balance, modelling of ice sheet dynamics and glaciers and for evaluating ice shelf stability, which merits its long-term monitoring. The line migrates both due to short term influences such as ocean tides and atmospheric pressure, and long-term effects such as changes of ice thickness, slope of bedrock and variations in sea level. Of the numerous in-situ and remote sensing methods currently in use to map the grounding line, Differential Interferometric Synthetic Aperture Radar (DInSAR) is, by far, the most accurate technique which produces spatially dense delineations. Tidal deformation at the ice sheet-ice shelf boundary is visible as a dense fringe belt in DInSAR interferograms and its landward limit is taken as a good approximation of the grounding line location (GLL). The GLL is usually manually digitized on the interferograms by human operators. This is both time consuming and introduces inconsistencies due to subjective interpretation especially in low coherence interferograms. On a large scale and with increasing data availability a key challenge is the automation of the delineation procedure. So far, a limited amount of studies were published regarding the delineation processes of typical features on the ice sheets using deep neural networks (DNNs). The objectives of this thesis were to further explore the feasibility of using machine learning for mapping the interferometric grounding line, as well as exploring the contributions of complementary features such as coherence estimated from phase, Digital Elevation Model, ice velocity, tidal displacement and atmospheric pressure, in addition to DInSAR interferograms. A dataset composed of manually delineated GLLs generated within ESA’s Antarctic Ice Sheet Climate Change Initiative project and corresponding DInSAR interferograms from ERS-1/2, Sentinel-1 and TerraSAR-X missions over Antarctica together with the above mentioned features was compiled and used for training two DNNs: Holistically-Nested Edge Detection (HED) andUNet. The developed processing chain handles creation of the training feature stack, training the DNNs and performing post processing functions on the resulting predictions. HED outperformed UNet and was able to achieve a median deviation (from manual delineations) of 209.23 m with a median absolute deviation of 152.91 m. Analysis of the individual feature contributions revealed that only the phase and derived features (real and imaginary interferogram components and coherence estimates) substantially influence the predicted GLLs. This finding is advantageous in terms of saving time, computational effort and memory in creating and storing the above mentioned feature stack. Although the delineations generated from HED do not perfectly follow the true GLL in all locations, the gains in efficiency and consistency are considerable, compared to the time and effort spent for manual digitizations. This study shows the potential of DNNs for automating the interferometric GLL delineation process

The Sleeping Giant: Measuring Ocean-Ice Interactions in Antarctica

Global sea level rise threatens to be one of the most costly consequences of human-caused climate change. And yet, projections of sea level rise remain poorly understood and highly uncertain. The largest potential contribution to global sea level rise involves the loss of ice covering all or even a portion of Antarctica. As global atmospheric and ocean temperatures rise, physical processes related to the ocean’s circulation: (i) carry this additional heat into the deep ocean, (ii) transport it poleward via the overturning circulation and (iii) ultimately deliver the heat to the underside of floating Antarctic ice shelves. Enhanced melting that occurs due to warm ocean waters plays an important role in the loss of ice from the continent. Our understanding of the first two steps that bring heat towards Antarctica has increased substantially over the past two decades through improved measurements of air-sea interactions and interior ocean properties (e.g., Argo). Yet, the constraints on the oceanic delivery of heat to Antarctic ice shelves and its impact on melt rates remains critically under-studied. Our inability to constrain the rate of retreat of Antarctic glaciers and how the Antarctic Ice Sheet will behave in a warming climate remains the single most significant reason for the large uncertainty in sea level projections over the 21st century. This problem is the focus of the KISS study, "The Sleeping Giant: Measuring Ocean Ice Interactions in Antarctica," and stands as one of the grand challenges of climate science today

Long-term observations of terminus position change, structural glaciology and velocity at Ninnis Glacier, George V Land, East Antarctica (1963-2021)

Over the last four decades, some major East Antarctic outlet glaciers have undergone rates of retreat, thinning and acceleration in response to ocean-climatic forcing. However, some major East Antarctic outlet glaciers remain unstudied in the recent past. Ninnis Glacier is one East Antarctic outlet glacier that is potentially vulnerable to future ocean-climate change and requires monitoring. This thesis quantifies and analyses long-term (1963-2021) changes in terminus position, structural glaciology and velocity at Ninnis Glacier. The results of this study show that Ninnis underwent three major calving events (in 1972-1974, 1998 and 2018), characterised by a 20–25-year periodicity and indicative of a naturally occurring cycle. Each respective calving event created a large-scale tabular iceberg and formed a new terminus position at similar locations up-ice relative to Ninnis’ 1992 grounding line position. The major calving events in 1998 and 2018 were controlled by the development of a central rift system that appears in the same location on Ninnis’ tongue, reinforcing the notion of a predictable calving cycle. Ice flow velocity trends before the 2018 calving event (2017-2018) revealed no discernible change in velocity immediately up-ice (+0.2 %) and down-ice (>0 %) of the 1992 grounding line, suggesting that rifting took place within a ‘passive’ sector of Ninnis’ ice tongue. Between 2018 and 2021, Ninnis underwent a pervasive deceleration up-ice (-2.1 %) and down-ice (-1.4 %) of the 1992 grounding line and on the distal ice tongue (-18.7 %). This indicated that the 2018 calving event did not result in the loss of dynamically important ice. Although Ninnis has previously been deemed a sector at risk of retreat, it is concluded that Ninnis is not currently undergoing Marine Ice Sheet Instability and is not currently sensitive to external forcing. This is consistent with low basal melt rates, negligible grounding line retreat and low thermal forcing temperatures in the coastal waters observed at Ninnis