ORCID Profile
0000-0003-3047-9908
Current Organisation
Northumbria University
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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: 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: 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: 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: Springer Science and Business Media LLC
Date: 31-03-2021
DOI: 10.1038/S41467-021-22259-0
Abstract: A potentially irreversible threshold in Antarctic ice shelf melting would be crossed if the ocean cavity beneath the large Filchner–Ronne Ice Shelf were to become flooded with warm water from the deep ocean. Previous studies have identified this possibility, but there is great uncertainty as to how easily it could occur. Here, we show, using a coupled ice sheet-ocean model forced by climate change scenarios, that any increase in ice shelf melting is likely to be preceded by an extended period of reduced melting. Climate change weakens the circulation beneath the ice shelf, leading to colder water and reduced melting. Warm water begins to intrude into the cavity when global mean surface temperatures rise by approximately 7 °C above pre-industrial, which is unlikely to occur this century. However, this result should not be considered evidence that the region is unconditionally stable. Unless global temperatures plateau, increased melting will eventually prevail.
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.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Sebastian Rosier.