Quaternary: ice sheets and their legacy

2018 ◽  
pp. 429-467 ◽  
Author(s):  
John A. Catt ◽  
Philip L. Gibbard ◽  
John J. Lowe ◽  
Danny McCarroll ◽  
James D. Scourse ◽  
...  
Keyword(s):  
Ice Sheets

A topological approach to the strain-rate pattern of ice sheets

1993 ◽  
Vol 39 (131) ◽  
pp. 10-14 ◽  
Author(s):  
J. F. Nye
Keyword(s):  
Strain Rate ◽  
Ice Sheets ◽  
Ice Sheet ◽  
Point Of View ◽  
Principal Strain ◽  
Topological Approach ◽  
Contour Maps ◽  
Isotropic Point ◽  
Horizontal Strain

AbstractThe pattern of horizontal strain rate in an ice sheet is discussed from a topological point of view. In a circularly symmetric ice sheet, the isotropic point for strain rate at its centre is degenerate and structurally unstable. On perturbation the degenerate point splits into two elementary isotropic points, each of which has the lemon pattern for the trajectories of principal strain rate. Contour maps of principal strain-rate values are presented which show the details of the splitting.

Analysis and Simulation of Altimeter Performance for the Production of Ice Sheet Topographic Maps

1986 ◽  
Vol 8 ◽  
pp. 141-145 ◽  
Author(s):  
K.C. Partington ◽  
C.G. Rapley
Keyword(s):  
High Precision ◽  
Systems Analysis ◽  
Ice Sheets ◽  
Ice Sheet ◽  
Data Interpretation ◽  
Topographic Maps ◽  
Error Signal ◽  
Tracking Systems ◽  
Tracking Mode ◽  
Non Linear

Satellite-borne, radar altimeters have already demonstrated an ability to produce high-precision, topographic maps of the ice sheets. Seasat operated in a tracking mode, designed for use over oceans, but successfully tracked much of the flatter regions of the ice sheet to ± 72° latitude. ERS-1 will extend coverage to ± 82° latitude and will be equipped with an ocean mode similar to that of Seasat and an ice mode designed to permit tracking of the steeper, peripheral regions. The ocean mode will be used over the flatter regions, because of its greater precision.Altimeter performance over the ice sheets has been investigated through a study of Seasat tracking behaviour and the use of an altimeter performance simulator, with a view to assessing the likely performance of ERS-1 and the design of improved tracking systems. Analysis of Seasat data shows that lock was frequently lost, as a result of possessing a non-linear height error signal over the width of the range window. Having lost lock, the tracker frequently failed to transfer rapidly and effectively to track mode. Use of the altimeter performance simulator confirms many of the findings from Seasat data and it is being used to facilitate data interpretation and mapping, through the modelling of waveform sequence.

Antarctic Climate History and Global Climate Changes

2017 ◽  
Author(s):  
Pontus Lurcock ◽  
Fabio Florindo
Keyword(s):  
Ocean Circulation ◽  
Climate Changes ◽  
Numerical Models ◽  
Global Climate ◽  
Sediment Cores ◽  
Ice Sheets ◽  
Global Ocean ◽  
Climate History ◽  
Global Climate Changes ◽  
Planetary Albedo

Antarctic climate changes have been reconstructed from ice and sediment cores and numerical models (which also predict future changes). Major ice sheets first appeared 34 million years ago (Ma) and fluctuated throughout the Oligocene, with an overall cooling trend. Ice volume more than doubled at the Oligocene-Miocene boundary. Fluctuating Miocene temperatures peaked at 17–14 Ma, followed by dramatic cooling. Cooling continued through the Pliocene and Pleistocene, with another major glacial expansion at 3–2 Ma. Several interacting drivers control Antarctic climate. On timescales of 10,000–100,000 years, insolation varies with orbital cycles, causing periodic climate variations. Opening of Southern Ocean gateways produced a circumpolar current that thermally isolated Antarctica. Declining atmospheric CO2 triggered Cenozoic glaciation. Antarctic glaciations affect global climate by lowering sea level, intensifying atmospheric circulation, and increasing planetary albedo. Ice sheets interact with ocean water, forming water masses that play a key role in global ocean circulation.

Shallow-Layers-Detection Ice Sounding Radar for Mapping of Polar Ice Sheets

2021 ◽  
pp. 1-10
Author(s):  
Bo Zhao ◽  
Yueyi Zhang ◽  
Shinan Lang ◽  
Yan Liu ◽  
Feng Zhang ◽  
...  
Keyword(s):  
Ice Sheets ◽  
Polar Ice ◽  
Polar Ice Sheets

Spatial variation of East Asian winter monsoon evolution between northern and southern China since the last glacial maximum

2021 ◽  
pp. 1-14
Author(s):  
Qin Li ◽  
Haibin Wu ◽  
Jun Cheng ◽  
Shuya Zhu ◽  
Chunxia Zhang ◽  
...  
Keyword(s):  
East Asian Winter Monsoon ◽  
Southern China ◽  
Ice Sheets ◽  
Northern China ◽  
Winter Monsoon ◽  
Asian Winter Monsoon ◽  
Spatial Differences ◽  
The Last Glacial Maximum ◽  
The Last Glacial

Abstract The East Asian winter monsoon (EAWM) is one of the most dynamic components of the global climate system. Although poorly understood, knowledge of long-term spatial differences in EAWM variability during the glacial–interglacial cycles is important for understanding the dynamic processes of the EAWM. We reconstructed the spatiotemporal characteristics of the EAWM since the last glacial maximum (LGM) using a comparison of proxy records and long-term transient simulations. A loess grain-size record from northern China (a sensitive EAWM proxy) and the sea surface temperature gradient of an EAWM index in sediments of the southern South China Sea were compared. The data–model comparison indicates pronounced spatial differences in EAWM evolution, with a weakened EAWM since the LGM in northern China but a strengthened EAWM from the LGM to the early Holocene, followed by a weakening trend, in southern China. The model results suggest that variations in the EAWM in northern China were driven mainly by changes in atmospheric carbon dioxide (CO2) concentration and Northern Hemisphere ice sheets, whereas orbital insolation and ice sheets were important drivers in southern China. We propose that the relative importance of insolation, ice sheets, and atmospheric CO2 for EAWM evolution varied spatially within East Asia.

scholarly journals Attention Multi-Scale Network for Automatic Layer Extraction of Ice Radar Topological Sequences

2021 ◽  
Vol 13 (12) ◽  
pp. 2425
Author(s):  
Yiheng Cai ◽  
Dan Liu ◽  
Jin Xie ◽  
Jingxian Yang ◽  
Xiangbin Cui ◽  
...  
Keyword(s):  
Deep Learning ◽  
Global Climate ◽  
Ice Sheets ◽  
Ice Sheet ◽  
Sheet Thickness ◽  
Learning Methods ◽  
Convolutional Network ◽  
Multi Scale ◽  
Ice Surface ◽  
Layer Extraction

Analyzing the surface and bedrock locations in radar imagery enables the computation of ice sheet thickness, which is important for the study of ice sheets, their volume and how they may contribute to global climate change. However, the traditional handcrafted methods cannot quickly provide quantitative, objective and reliable extraction of information from radargrams. Most traditional handcrafted methods, designed to detect ice-surface and ice-bed layers from ice sheet radargrams, require complex human involvement and are difficult to apply to large datasets, while deep learning methods can obtain better results in a generalized way. In this study, an end-to-end multi-scale attention network (MsANet) is proposed to realize the estimation and reconstruction of layers in sequences of ice sheet radar tomographic images. First, we use an improved 3D convolutional network, C3D-M, whose first full connection layer is replaced by a convolution unit to better maintain the spatial relativity of ice layer features, as the backbone. Then, an adjustable multi-scale module uses different scale filters to learn scale information to enhance the feature extraction capabilities of the network. Finally, an attention module extended to 3D space removes a redundant bottleneck unit to better fuse and refine ice layer features. Radar sequential images collected by the Center of Remote Sensing of Ice Sheets in 2014 are used as training and testing data. Compared with state-of-the-art deep learning methods, the MsANet shows a 10% reduction (2.14 pixels) on the measurement of average mean absolute column-wise error for detecting the ice-surface and ice-bottom layers, runs faster and uses approximately 12 million fewer parameters.

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