Noise Removal and Feature Extraction in Airborne Radar Sounding Data of Ice Sheets

The airborne ice-penetrating radar (IPR) is an effective method used for ice sheet exploration and is widely applied for detecting the internal structures of ice sheets and for understanding the mechanism of ice flow and the characteristics of the bottom of ice sheets. However, because of the ambient influence and the limitations of the instruments, IPR data are frequently overlaid with noise and interference, which further impedes the extraction of layer features and the interpretation of the physical characteristics of the ice sheet. In this paper, we first applied conventional filtering methods to remove the feature noise and interference in IPR data. Furthermore, machine learning methods were introduced in IPR data processing for noise removal and feature extraction. Inspired by a comparison of the filtering methods and machine learning methods, we propose a fusion method combining both filtering methods and machine-learning-based methods to optimize the feature extraction in IPR data. Field data tests indicated that, under different conditions of IPR data, the application of different methods and strategies can improve the layer feature extraction.

“Sticky” Ice Sheets May Have Led to More Intense Glacial Cycles

New research attributes a shift to longer, stronger glacial cycles to increased friction between ice sheets and bedrock in the Northern Hemisphere 1 million years ago.

Reviewing the elements of marine ice cliff instability

Antarctica’s ice sheets are the largest potential sea-level rise contributors, but projections of future sea-level rise yield wide ranges of estimates under different emission scenarios. An important factor in the variability of estimates is marine ice cliff instability (MICI). Inclusion of MICI yields the highest potential sea-level rise cases but also the largest uncertainty due to poor understanding of the factors that control it and the mechanisms of how it happens. Although evidence for MICI has been implied by paleo-ice sheet studies and observations of keel plough mark on sea-floor, recent statistical and modelling studies have suggested a lower magnitude of MICI effect on sea-level rise due to thinning of ice sheets and buttressing forces exerted on potentially failing cliffs. This paper reviews the factors that control MICI with the goal of identifying priorities for modern ice sheet studies to better bound the estimates.

The geoPebble System: Design and Implementation of a Wireless Sensor Network of GPS-Enabled Seismic Sensors for the Study of Glaciers and Ice Sheets

The geoPebble system is a network of wirelessly interconnected seismic and GPS sensor nodes with geophysical sensing capabilities for the study of ice sheets in Antarctica and Greenland, as well as mountain glaciers. We describe our design methodology, which has enabled us to develop these state-of-the art units using commercial-off-the-shelf hardware combined with custom-designed hardware and software. Each geoPebble node is a self-contained, wirelessly connected sensor for collecting seismic activity and position information. Each node is built around a three-component seismic recorder, which includes an amplifier, filter, and 24-bit analog-to-digital converter that can sample incoming seismic signals up to 10 kHz. The timing for each node is available from GPS measurements and a local precision oscillator that is conditioned by the GPS timing pulses. In addition, we record the carrier-phase measurement of the L1 GPS signal in order to determine location at sub-decimeter accuracy (relative to other geoPebble nodes within a radius of a few kilometers). Each geoPebble includes 32 GB of solid-state storage, wireless communications capability to a central supervisory unit, and auxiliary measurements capability (including tilt from accelerometers, absolute orientation from magnetometers, and temperature). The geoPebble system has been successfully validated in the field in Antarctica and Greenland.

Deep learning speeds up ice flow modelling by several orders of magnitude

This paper introduces the Instructed Glacier Model (IGM) – a model that simulates ice dynamics, mass balance and its coupling to predict the evolution of glaciers, icefields or ice sheets. The novelty of IGM is that it models the ice flow by a Convolutional Neural Network, which is trained from data generated with hybrid SIA + SSA or Stokes ice flow models. By doing so, the most computationally demanding model component is substituted by a cheap emulator. Once trained with representative data, we demonstrate that IGM permits to model mountain glaciers up to 1000 × faster than Stokes ones on Central Processing Units (CPU) with fidelity levels above 90% in terms of ice flow solutions leading to nearly identical transient thickness evolution. Switching to the GPU often permits additional significant speed-ups, especially when emulating Stokes dynamics or/and modelling at high spatial resolution. IGM is an open-source Python code which deals with two-dimensional (2-D) gridded input and output data. Together with a companion library of trained ice flow emulators, IGM permits user-friendly, highly efficient and mechanically state-of-the-art glacier and icefields simulations.

Brief communication: A roadmap towards credible projections of ice sheet contribution to sea level

Accurately projecting mass loss from ice sheets is of critical societal importance. However, despite recent improvements in ice sheet models, our analysis of a recent effort to project ice sheet contribution to future sea level suggests that few models reproduce historical mass loss accurately and that they appear much too confident in the spread of predicted outcomes. The inability of models to reproduce historical observations raises concerns about the models' skill at projecting mass loss. Here we suggest that uncertainties in the future sea level contribution from Greenland and Antarctica may well be significantly higher than reported in that study. We propose a roadmap to enable a more realistic accounting of uncertainties associated with such forecasts and a formal process by which observations of mass change should be used to refine projections of mass change. Finally, we note that tremendous government investment and planning affecting tens to hundreds of millions of people is founded on the work of just a few tens of scientists. To achieve the goal of credible projections of ice sheet contribution to sea level, we strongly believe that investment in research must be commensurate with the scale of the challenge.

Signature of ice melt over the Greenland derived from MSMR (OCEANSAT-1) data

In order to monitor the impact of global warming phenomena over the Polar Regions, it is necessary to monitor snow/ice melt on the Greenland and the Antarctic ice sheets. Using MSMR data, it is possible to differentiate sea ice at different concentration levels. On the basis of microwave emissivities of continental ice and sea ice, useful information on the formation and melting of the ice can be derived. The paper discusses different strategies to derive a melt signal from the MSMR observations for the continental ice sheets in Greenland. The Polarization Difference (PD) for 21 GHz, available from MSMR data, is studied and an appropriate threshold is selected to detect the presence of melt signal. The results of the present study have bearing on climate changes.

Reconciling the Apparent Absence of a Last Glacial Maximum Alpine Glacial Advance, Yukon Territory, Canada, through Cosmogenic Beryllium-10 and Carbon-14 Measurements

We present a new in situ produced cosmogenic beryllium-10 and carbon-14 nuclide chronology from two sets (outer and inner) of alpine glacier moraines from the Grey Hunter massif of southern Yukon Territory, Canada. The chronology potential of moraines deposited by alpine glaciers outside the limits of the Last Glacial Maximum (LGM) ice sheets potentially provide a less-ambiguous archive of mass balance, and hence climate than can be inferred from the extents of ice sheets themselves. Results for both nuclides are inconclusive for the outer moraines, with evidence for pre-LGM deposition (beryllium-10) and Holocene deposition (carbon-14). Beryllium-10 results from the inner moraine are suggestive of canonical LGM deposition, but with relatively high scatter. Conversely, in situ carbon-14 results from the inner moraines are tightly clustered and suggestive of terminal Younger Dryas deposition. We explore plausible scenarios leading to the observed differences between nuclides and find that the most parsimonious explanation for the outer moraines is that of pre-LGM deposition, but many of the sampled boulder surfaces were not exhumed from within the moraine until the Holocene. Our results thus imply that the inner and outer moraines sampled pre- and post-date the canonical LGM and that moraines dating to the LGM are lacking likely due to overriding by the subsequent Late Glacial/earliest Holocene advance.