ORCID Profile
0000-0003-4236-5369
Current Organisations
University of Iceland
,
Northumbria University
,
Swiss Federal Institute of Technology in Zurich
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Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-15264
Abstract: Through their role in buttressing upstream ice flow, Antarctic ice shelves play an important part in regulating future sea level change. Reduction in ice-shelf buttressing caused by increased ocean-induced melt along their undersides is now understood to be one of the key drivers of ice loss from the Antarctic Ice Sheet. However, despite the importance of this forcing mechanism, most ice-sheet simulations currently rely on simple melt-parametrisations of this ocean-driven process since a fully coupled ice-ocean modelling framework is prohibitively computationally expensive. Here, we provide an alternative approach that can capture the greatly improved physical description of this process provided by large-scale ocean-circulation models over currently employed melt-parameterisations, but with trivial computational expense.& This new method brings together deep learning and physical modelling to develop a deep neural network framework, MELTNET, that can emulate ocean model predictions of sub-ice shelf melt rates. We train MELTNET on synthetic geometries, using the NEMO ocean model as a ground-truth in lieu of observations to provide melt rates both for training and to evaluate the performance of the trained network. We show that MELTNET can accurately predict melt rates for a wide range of complex synthetic geometries, with a normalized root mean squared error of 0.11m/yr compared to the ocean model. MELTNET calculates melt rates several orders of magnitude faster than the ocean model and outperforms more traditional parameterisations for 96% of geometries tested. Furthermore, we find MELTNET's melt rate estimates show sensitivity to established physical relationships such as changes in thermal forcing and ice shelf slope. This study demonstrates the potential for a deep learning framework to calculate melt rates with almost no computational expense, that could in the future be used in conjunction with an ice sheet model to provide predictions for large-scale ice sheet models.
Publisher: Cambridge University Press (CUP)
Date: 21-07-2016
DOI: 10.1017/JOG.2016.77
Abstract: The dominant mass-loss process on the Antarctic Peninsula has been ice-shelf collapse, including the Larsen A Ice Shelf in early 1995. Following this collapse, there was rapid speed up and thinning of its tributary glaciers. We model the impact of this ice-shelf collapse on upstream tributaries, and compare with observations using new datasets of surface velocity and ice thickness. Using a two-horizontal-dimension shallow shelf approximation model, we are able to replicate the observed large increase in surface velocity that occurred within Drygalski Glacier, Antarctic Peninsula. The model results show an instantaneous twofold increase in flux across the grounding line, caused solely from the reduction in backstress through ice shelf removal. This demonstrates the importance of ice-shelf buttressing for flow upstream of the grounding line and highlights the need to explicitly include lateral stresses when modelling real-world settings. We hypothesise that further increases in velocity and flux observed since the ice-shelf collapse result from transient mass redistribution effects. Reproducing these effects poses the next, more stringent test of glacier and ice-sheet modelling studies.
Publisher: Copernicus GmbH
Date: 12-01-2022
DOI: 10.5194/TC-2021-396
Abstract: Abstract. Through their role in buttressing upstream ice flow, Antarctic ice shelves play an important part in regulating future sea level change. Reduction in ice-shelf buttressing caused by increased ocean-induced melt along their undersides is now understood to be one of the key drivers of ice loss from the Antarctic Ice Sheet. However, despite the importance of this forcing mechanism most ice-sheet simulations currently rely on simple melt-parametrisations of this ocean-driven process, since a fully coupled ice-ocean modelling framework is prohibitively computationally expensive. Here, we provide an alternative approach that is able to capture the greatly improved physical description of this process provided by large-scale ocean-circulation models over currently employed melt-parameterisations but with trivial computational expense. We introduce a new approach that brings together deep learning and physical modelling to develop a deep neural network framework, MELTNET, that can emulate ocean model predictions of sub-ice shelf melt rates. We train MELTNET on synthetic geometries, using the NEMO ocean model as a ground-truth in lieu of observations to provide melt rates both for training and to evaluate the performance of the trained network. We show that MELTNET can accurately predict melt rates for a wide range of complex synthetic geometries and outperforms more traditional parameterisations for 95 % of geometries tested. Furthermore, we find MELTNET's melt rate estimates show sensitivity to established physical relationships such as a changes in thermal forcing and ice shelf slope. This study demonstrates the potential for a deep learning framework to calculate melt rates with almost no computational expense, that could in the future be used in conjunction with an ice sheet model to provide predictions for large-scale ice sheet models.
Publisher: Copernicus GmbH
Date: 17-07-2017
DOI: 10.5194/ESSD-2017-70
Abstract: Abstract. We present a compilation of GPS time series, including those for previously unpublished sites, showing that flow across the entire Ronne Ice Shelf and its adjoining ice streams is strongly affected by ocean tides. Previous observations have shown strong diurnal and semidiurnal motion of the ice shelf and surface flow speeds of Rutford Ice Stream (RIS) are known to vary with a fortnightly (Msf) periodicity. Our new dataset shows that the Msf flow modulation, first observed on RIS, is also found on Evans, Talutis, Institute and Foundation Ice Streams, i.e. on all ice streams for which data are available. The litude of the Msf signal increases downstream of grounding lines, reaching up to 20 % of mean flow speeds where ice streams feed into the main shelf. Upstream of ice stream grounding lines, decay length scales are relatively uniform for all ice streams but the speed at which the Msf signal propagates upstream shows more variation. Observations and modelling of tidal variations in ice flow can help constrain crucial parameters that determine the rate and extent of potential ice mass loss from Antarctica. Given that the Msf modulation in ice flow is readily observed across the entire region, at distances of up to 80 km upstream of grounding lines, but is not completely reproduced in any existing numerical model, this new dataset suggests a pressing need to identify the missing processes responsible for its generation and propagation. The new GPS data set is publicly available through the UKPDC at 0.5285/4fe11286-0e53-4a03-854c-a79a44d1e356.
Publisher: American Geophysical Union (AGU)
Date: 23-05-2012
DOI: 10.1029/2012GL051636
Publisher: Springer Science and Business Media LLC
Date: 12-12-2019
Publisher: American Geophysical Union (AGU)
Date: 21-04-2011
DOI: 10.1029/2011GL046680
Publisher: Copernicus GmbH
Date: 25-07-2016
Abstract: Abstract. Coupled ice sheet–ocean models capable of simulating moving grounding lines are just becoming available. Such models have a broad range of potential applications in studying the dynamics of marine ice sheets and tidewater glaciers, from process studies to future projections of ice mass loss and sea level rise. The Marine Ice Sheet–Ocean Model Intercomparison Project (MISOMIP) is a community effort aimed at designing and coordinating a series of model intercomparison projects (MIPs) for model evaluation in idealized setups, model verification based on observations, and future projections for key regions of the West Antarctic Ice Sheet (WAIS). Here we describe computational experiments constituting three interrelated MIPs for marine ice sheet models and regional ocean circulation models incorporating ice shelf cavities. These consist of ice sheet experiments under the Marine Ice Sheet MIP third phase (MISMIP+), ocean experiments under the Ice Shelf-Ocean MIP second phase (ISOMIP+) and coupled ice sheet–ocean experiments under the MISOMIP first phase (MISOMIP1). All three MIPs use a shared domain with idealized bedrock topography and forcing, allowing the coupled simulations (MISOMIP1) to be compared directly to the in idual component simulations (MISMIP+ and ISOMIP+). The experiments, which have qualitative similarities to Pine Island Glacier Ice Shelf and the adjacent region of the Amundsen Sea, are designed to explore the effects of changes in ocean conditions, specifically the temperature at depth, on basal melting and ice dynamics. In future work, differences between model results will form the basis for the evaluation of the participating models.
Publisher: Copernicus GmbH
Date: 07-02-2023
Abstract: Abstract. Through their role in buttressing upstream ice flow, Antarctic ice shelves play an important part in regulating future sea-level change. Reduction in ice-shelf buttressing caused by increased ocean-induced melt along their undersides is now understood to be one of the key drivers of ice loss from the Antarctic ice sheet. However, despite the importance of this forcing mechanism, most ice-sheet simulations currently rely on simple melt parameterisations of this ocean-driven process since a fully coupled ice–ocean modelling framework is prohibitively computationally expensive. Here, we provide an alternative approach that is able to capture the greatly improved physical description of this process provided by large-scale ocean-circulation models over currently employed melt parameterisations but with trivial computational expense. This new method brings together deep learning and physical modelling to develop a deep neural network framework, MELTNET, that can emulate ocean model predictions of sub-ice-shelf melt rates. We train MELTNET on synthetic geometries, using the NEMO ocean model as a ground truth in lieu of observations to provide melt rates both for training and for evaluation of the performance of the trained network. We show that MELTNET can accurately predict melt rates for a wide range of complex synthetic geometries, with a normalised root mean squared error of 0.11 m yr−1 compared to the ocean model. MELTNET calculates melt rates several orders of magnitude faster than the ocean model and outperforms more traditional parameterisations for 96 % of geometries tested. Furthermore, we find MELTNET's melt rate estimates show sensitivity to established physical relationships such as changes in thermal forcing and ice-shelf slope. This study demonstrates the potential for a deep learning framework to calculate melt rates with almost no computational expense, which could in the future be used in conjunction with an ice sheet model to provide predictions for large-scale ice sheet models.
Publisher: Copernicus GmbH
Date: 03-2013
Abstract: Abstract. Full Stokes flow-line models predict that fast-flowing ice streams transmit information about their bedrock topography most efficiently to the surface for basal undulations with length scales between 1 and 20 times the mean ice thickness. This typical behaviour is independent of the precise values of the flow law and sliding law exponents, and should be universally observable. However, no experimental evidence for this important theoretical prediction has been obtained so far, hence ignoring an important test for the physical validity of current-day ice flow models. In our work we use recently acquired airborne radar data for the Rutford Ice Stream and Evans Ice Stream, and we show that the surface response of fast-flowing ice is highly sensitive to bedrock irregularities with wavelengths of several ice thicknesses. The sensitivity depends on the slip ratio, i.e. the ratio between mean basal sliding velocity and mean deformational velocity. We find that higher values of the slip ratio generally lead to a more efficient transfer, whereas the transfer is significantly d ened for ice that attains most of its surface velocity by creep. Our findings underline the importance of bedrock topography for ice stream dynamics on spatial scales up to 20 times the mean ice thickness. Our results also suggest that local variations in the flow regime and surface topography at this spatial scale cannot be explained by variations in basal slipperiness.
Publisher: Copernicus GmbH
Date: 20-11-2017
Abstract: Abstract. We present a compilation of GPS time series, including those for previously unpublished sites, showing that flow across the entire Ronne Ice Shelf and its adjoining ice streams is strongly affected by ocean tides. Previous observations have shown strong horizontal diurnal and semidiurnal motion of the ice shelf, and surface flow speeds of Rutford Ice Stream (RIS) are known to vary with a fortnightly (Msf) periodicity. Our new data set shows that the Msf flow modulation, first observed on RIS, is also found on Evans, Talutis, Institute, and Foundation ice streams, i.e. on all ice streams for which data are available. The litude of the Msf signal increases downstream of grounding lines, reaching up to 20 % of mean flow speeds where ice streams feed into the main ice shelf. Upstream of ice stream grounding lines, decay length scales are relatively uniform for all ice streams but the speed at which the Msf signal propagates upstream shows more variation. Observations and modelling of tidal variations in ice flow can help constrain crucial parameters that determine the rate and extent of potential ice mass loss from Antarctica. Given that the Msf modulation in ice flow is readily observed across the entire region at distances of up to 80 km upstream of grounding lines, but is not completely reproduced in any existing numerical model, this new data set suggests a pressing need to identify the missing processes responsible for its generation and propagation. The new GPS data set is publicly available through the UK Polar Data Centre at 0.5285/4fe11286-0e53-4a03-854c-a79a44d1e356.
Publisher: American Geophysical Union (AGU)
Date: 15-06-2011
DOI: 10.1029/2011JC006949
Publisher: American Geophysical Union (AGU)
Date: 09-03-2021
DOI: 10.1029/2020GL091454
Abstract: Recent ice sheet mass loss in Antarctica has been attributed to an influx of warm ocean waters, which drove grounding‐line retreat and ice thinning. Episodic retreat and rapid thinning also occurred in the southwestern Ross Sea during the Holocene, which today accommodates cold ocean waters. We applied finite element ice‐flow modeling to investigate the roles of ocean temperature and bed topography in the deglaciation of this region. First, our experiments demonstrate that bed topography controlled the spatial pattern of grounding‐line retreat. Topographic pinning points limited the rate of ice loss until retreat progressed beyond a bathymetric threshold. Second, ocean thermal forcing determined the timing of this ice loss. Enhanced ocean‐driven melt is required during the Early‐to‐Mid Holocene to replicate geological records of deglaciation, possibly indicating that warm ocean waters were once present in this region. On multi‐centennial timescales, ocean temperature drove, while bed topography controlled, nonlinear rates of ice mass loss.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: Switzerland
No related grants have been discovered for Gudmundur Hilmar Gudmundsson.