CR2.9

EDI
Beyond the unconstrained: Driving and assisting cryospheric models with observations

This interdisciplinary session brings together modellers and observationalists to present results and exchange knowledge and experience in the use of inverse methods, geostatistics and data assimilation - including machine learning - in cryospheric science.
In numerous research fields it is now possible not only to deduce static features of a physical system but also to retrieve information on transient processes between different states or even regime shifts. In the cryospheric sciences a large potential for future developments lies at the intersection of observations and models with the aim to yield prognostic capabilities in space and time. Compared to other geoscientific disciplines like meteorology or oceanography, where techniques such as data assimilation have been well established for decades, in cryospheric sciences only the foundation has been laid for the use of these techniques, one reason often being the sparsity of observations.
We invite contributions from a wide range of methodologies - from satellite observations to deep-looking geophysical methods and advancements in numerical techniques, and from topics including permafrost, sea ice and snow to glaciers and ice sheets, covering static system characterisations as well as transient processes.

Co-organized by CL5.2/GI1/HS13
Convener: Olaf Eisen | Co-conveners: Nanna Bjørnholt Karlsson, Johannes SutterECSECS, Elisa MantelliECSECS
Presentations
| Thu, 26 May, 13:20–15:52 (CEST)
 
Room N2

Presentations: Thu, 26 May | Room N2

Chairpersons: Olaf Eisen, Nanna Bjørnholt Karlsson
13:20–13:23
13:23–13:30
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EGU22-10509
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ECS
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Virtual presentation
Brian Groenke, Moritz Langer, Guillermo Gallego, and Julia Boike

Over the past few decades, polar research teams around the world have deployed long-term measurement sites to monitor changes in permafrost environments. Many of these sites include borehole sensor arrays which provide measurements of ground temperature as deep as 50 meters or more below the surface. Recent studies have attempted to leverage these borehole data from the Global Terrestrial Network of Permafrost to quantify changes in permafrost temperatures at a global scale. However, temperature measurements provide an incomplete picture of the Earth's subsurface thermal regime. It is well known that regions with warmer permafrost, i.e. where mean annual ground temperatures are close to zero, often show little to no long-term change in ground temperature due to the latent heat effect. Thus, regions where the least warming is observed  may also be the most vulnerable to rapid permafrost thaw. Since direct measurements of soil moisture in the permafrost layer are not widely available, thermal modeling of the subsurface plays a crucial role in understanding how permafrost responds to changes in the local energy balance. In this work, we explore a new probabilistic method to link observed annual temperatures in boreholes to permafrost thaw via Bayesian parameter estimation and Monte Carlo simulation with a transient heat model. We apply our approach to several sites across the Arctic and demonstrate the impact of local landscape variability on the relationship between long term changes in temperature and latent heat.

How to cite: Groenke, B., Langer, M., Gallego, G., and Boike, J.: A probabilistic analysis of permafrost temperature trends with ensemble modeling of heat transfer, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10509, https://doi.org/10.5194/egusphere-egu22-10509, 2022.

13:30–13:37
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EGU22-8938
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ECS
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Virtual presentation
Élise Devoie, Stephan Gruber, and Jeffrey McKenzie

Objective: Estimate Soil Freezing Characteristic Curves (SFCCs) and uncertainty bounds based on a compilation of existing measured SFCCs.

Key Findings

  • Uncertainty in measured SFCCs is estimated based on measurement technique, water content, and soil disturbance
  • An open-source tool for estimating and constraining SFCCs is developed for use in parameterizing freeze/thaw models

Abstract

Cold-regions landscapes are undergoing rapid change due to a warming climate. This change is impacting many elements of the landscape and is often controlled by soil freeze/thaw processes. Soil freeze/thaw is governed by the Soil Freezing Characteristic Curve (SFCC) that relates the soil temperature to its unfrozen water content. This relation is needed in all physically based numerical models including soil freeze/thaw processes. A repository of all collected SFCC data and an R package for accessing and processing this data was presented in "A Repository of 100+ Years of Measured Soil Freezing Characteristic Curves".

This rich SFCC dataset is synthesized with a focus on potential sources of error due to the combination of measurement technique, data interpretation, and physical freeze-thaw process in a specific soil. Particular attention is given to combining sources of error and working with datasets given incomplete and missing metadata. A tool is developed to extract an SFCC for a soil with specified properties alongside its uncertainty bounds. This tool is intended for use in freeze/thaw models to improve freeze/thaw estimates, and better represent the ice and liquid water content of freezing soils. As phase change accounts for a vast majority of the energy budget in freezing soils, accurately representing the process is essential for realistic predictions. In addition, SFCC type curves are provided for the common sand, silt, clay, and organic soil textures when additional data is unavailable to define the SFCC more precisely.

How to cite: Devoie, É., Gruber, S., and McKenzie, J.: Constraining Soil Freezing Models using Observed Soil Freezing Characteristic Curves, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8938, https://doi.org/10.5194/egusphere-egu22-8938, 2022.

13:37–13:44
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EGU22-9262
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ECS
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Virtual presentation
Imke Sievers, Lars Stenseng, and Till Rasmussen
This presentation introduces a method to assimilate freeboard from radar satellite observations.
Many studies have shown that the skill and memory of sea ice models using sea ice thickness as initial condition improve compered to model runs only initializing sea ice concentration. The only Arctic wide sea ice thickness data which could be used for initialization is coming from satellite observations. Since sea ice can’t directly be measured from space freeboard data is used to derive sea ice thickness. Freeboard is converted under assumption of hydrostatic equilibrium to sea ice thickness. For this conversion snow thickness is needed. Due to a lack of Arctic wide snow cover observations most products use a snow climatology or a modification of one. This has proofed to introduce errors. To avoid the errors introduced by this method the presented work aims to assimilate freeboard directly. This presentation will introduce the method and show first results. The assimilation period overlaps with ICESat2 mission. We present a comparison between the presented freeboard assimilation and ICESat2 sea ice thickness products of a first winter season.

How to cite: Sievers, I., Stenseng, L., and Rasmussen, T.: Assimilating Cyrosat2 freeboard into a coupled ice-ocean model  , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9262, https://doi.org/10.5194/egusphere-egu22-9262, 2022.

13:44–13:51
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EGU22-2061
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ECS
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Presentation form not yet defined
Aliette Chenal, Charles-Emmanuel Testut, Florent Garnier, Parent Laurent, and Garric Gilles

Sea ice is a key element in our climate system, and it is very sensitive to the current observed climate change. Sea ice volume is a sensitive indicator of the health of Arctic although very challenging to estimate precisely since it is a combination of sea ice area and sea ice thickness. Arctic sea ice volume has decreased by as much as 75% at the end of the summer season if compared with the conditions 40 years ago. The ongoing decline of Arctic sea ice exposes the ocean to anomalous surface heat and freshwater fluxes that can have potential implication for the Arctic region and beyond, for the general oceanic circulation itself.

For more than a decade, Mercator Ocean International develops and produces Global Ocean Reanalysis with a 1/4° resolution system. Based on the NEMO modelling platform, observations are assimilated by a reduced-order Kalman filter. In-situ CORA database, altimetric data, sea surface temperature, and sea ice concentration are jointly assimilated to constrain the ocean and sea ice model.

In previous reanalysis, long-term sea ice volume drift has been observed in the Arctic. To obtain a better constraint on the sea ice thickness, Cryosat-2 radar Freeboard data are assimilated jointly with the sea ice concentration in a multidata/multivariate sea ice analysis. The coupled ocean and ice assimilation system runs on a 7-day cycle, using IAU (Incremental Analysis Update) and a 4D increment. The “white ocean” is modelled with the multi-categories LIM3.6 sea ice numerical model. The aim of this study is to initiate the development of the future operational multi-variate and multi-data sea ice analysis system with freeboard radar assimilation.

After describing this global sea ice reanalysis system, we present results on the abilities of this configuration to reproduce sea ice extent and volume interannual variability in both hemispheres. Comparisons between experiments with and without assimilation show that the joint assimilation of CryoSat-2 radar freeboard and sea ice concentration reduces most of model biases of sea ice thickness, e.g., in the north of the Canadian Arctic Archipelago and in the Beaufort Sea in the Arctic. Moreover, radar freeboard assimilation does not hinder the good results in simulating sea ice extent previously obtained with the assimilation of only sea ice concentration. Validation with non-assimilated satellite data and in-situ data supports these findings. Lastly, snow depth significantly influences the Freeboard measurement: this study also reveals the importance of including snow information on freeboard retrieval and on the ice volume assimilation methodology.

These experiments take place in a context of increasing interest in polar regions and prepare the launch of Copernicus Sentinel expansion satellite missions.

How to cite: Chenal, A., Testut, C.-E., Garnier, F., Laurent, P., and Gilles, G.: Assimilation of CryoSat-2 radar Freeboard data in a global ocean-sea ice modelling system., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2061, https://doi.org/10.5194/egusphere-egu22-2061, 2022.

13:51–13:58
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EGU22-5113
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ECS
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On-site presentation
Patrick Schmitt, Fabien Maussion, and Philipp Gregor

Ongoing global glacier retreat leads to sea-level rise and changes in regional freshwater availability. For an adequate adaptation to these changes, knowledge about the ice volume and the current dynamic state of glaciers is crucial. At regional to global scales, sparse observations made the dynamic state of glaciers very difficult to assess. Thanks to recent advances in global geodetic mass-balance and velocity assessments, new ways to initialize numerical models and ice thickness estimation emerge. In this contribution, we present the COst Minimization Bed INvErsion model (COMBINE), which aims to be a cheap, flexible global data assimilation and inversion method. COMBINE uses an existing numerical model of glacier evolution (the Open Global Glacier Model, OGGM) rewritten in the machine learning framework PyTorch. This makes the model fully differentiable and allows to iteratively minimize a cost function penalizing mismatch to observations. Thanks to the flexible nature of automatic differentiation, various observational sources distributed in time can be considered (e.g. surface elevation and area changes, ice velocities). No assumption about the dynamic glacier state is needed, releasing the equilibrium assumption often required for large scale ice volume computations. In this contribution, we will demonstrate the capabilities of COMBINE in several idealized and real-world applications, and discuss its added value and upcoming challenges for operational application.

How to cite: Schmitt, P., Maussion, F., and Gregor, P.: Estimating large scale dynamic mountain glacier states with numerical modelling and data assimilation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5113, https://doi.org/10.5194/egusphere-egu22-5113, 2022.

13:58–14:05
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EGU22-6368
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Presentation form not yet defined
On the systematic utilisation of remotely sensed observations in a coupled glacier system model
(withdrawn)
Johannes J. Fürst, Theresa Diener, and Fabien Gillet-Chaulet
14:05–14:12
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EGU22-9886
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ECS
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Virtual presentation
Jogscha Abderhalden and Irina Rogozhina

No continuously updated glacier and glacial lake inventories exist for Norway. Previous inventories have been developed for the time periods of 1947-1985, 1988-1997 and 1999-2006 for glaciers and 1988-1997, 1999-2006, 2014 and 2018 for glacial lakes, by manual digitization, and semi-automated mapping. However, these methods are both time consuming and do not allow for an analysis of glacial lake behaviour on shorter timescales or on a seasonal basis. Therefore, one aim of this study is to present consistent inventories for glaciers and glacial lakes in Norway using semi-automated mapping and machine learning techniques applied on satellite imagery of different spatial and temporal resolution (Landsat 30m, 16 days, and Sentinel 10m, 5 days). An automated method that allows frequent monitoring of glacier variables can provide essential knowledge for the understanding of glacial lake dynamics in a changing climate.

In addition to glacial lake inventories, smaller ice caps with active glacial lakes are investigated more closely, aiming at following the development of glacial lakes throughout seasons. Here we are also analyzing the suitability of PlanetScope imagery compared to the Sentinel and Landsat imagery to detect the known glacial lake outburst flood events and identify currently unrecognized hazard-prone glacial lakes. Since the field-based investigations of glacial lake changes (especially of the ice-dammed lakes) are sparse in Norway, developing methods for remote-sensed, automated monitoring of glacial lake changes and glacial lake outburst floods is essential in order to develop early warning systems, detect potentially hazardous lakes and prevent human losses and damages to infrastructure and local businesses.

How to cite: Abderhalden, J. and Rogozhina, I.: Automated Tracking of Glacial Lake Outburst Floods in Norway, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9886, https://doi.org/10.5194/egusphere-egu22-9886, 2022.

14:12–14:19
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EGU22-753
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ECS
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On-site presentation
Samuel Cook and Fabien Gillet-Chaulet

Providing suitable initial states is a long-standing problem in numerical modelling of glaciers and ice sheets, as well as in other areas of the geosciences, due to the frequent lack of observations. This is particularly acute in glaciology, where important parameters such as the underlying bed may be only very sparsely observed or even completely unobserved. Glaciological models also often require lengthy relaxation periods to dissipate incompatibilities between input datasets gathered over different timeframes, which may lead to the modelled initial state diverging significantly from the real state of the glacier, with consequent effects on the accuracy of the simulation. Sequential data assimilation using an ensemble offers one possibility for resolving both these issues: by running the model over a period for which various observational datasets are available and loading observations into the model at the time they were gathered, the model state can be brought into good agreement with the real glacier state at the end of the observational window. The mean values of the ensemble for unknown parameters, such as the bed, then also represent best guesses for the true parameter values. This assimilated model state can then be used to initialise prognostic runs without introducing model artefacts or a distorted picture of the actual glacier.

In this study, we present a framework for conducting sequential data assimilation and retrieving the bed of a glacier in a 3D setting of the open-source, finite-element glacier flow model, Elmer/Ice, and solving the Stokes equations rather than using the shallow shelf approximation. Assimilation is undertaken using the open-source PDAF library developed at the Alfred Wegener Institute. We demonstrate that the set-up allows us to accurately retrieve the bed of a synthetic glacier and present our plans to extend it to a real-world example.

How to cite: Cook, S. and Gillet-Chaulet, F.: 3D sequential data assimilation in Elmer/Ice with Stokes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-753, https://doi.org/10.5194/egusphere-egu22-753, 2022.

14:19–14:26
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EGU22-3743
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ECS
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On-site presentation
Daniel Richards, Sam Pegler, and Sandra Piazolo

Accurately predicting ice crystal fabrics is key to understanding the processes and deformation in ice-sheets. Here we use SpecCAF, a continuum fabric evolution model validated against laboratory experiments, to predict the fabric evolution with an active ice stream. This is done by predicting the fabrics at the East Greenland Ice core Project (EGRIP) site. We do this using satellite data and inferred particle paths, combined with the shallow ice approximation (with basal slip) to infer a leading order approximation for the deformation through the ice sheet. We find that SpecCAF is able to predict the patterns observed at EGRIP - a girdle/horizontal maxima fabric perpendicular to the flow direction. By reducing the rate of rotational recrystallization in the model we are also able to predict the fabric strength at EGRIP. This suggests the effect of rotational recrystallization on the fabric may be primarily strain-rate/stress dependent. These results show SpecCAF can be applied to real-world conditions and provide insights into the deformation and basal-conditions of the ice sheet. As the model only considers deformation and recrystallization through dislocation creep, the results imply that - for the ice stream modelled - no other process is significantly influencing both the produced ice fabric and the deformation. We find that the model gives best results for full slip at the base of the ice sheet, implying that the level of sliding at the base of the ice sheet in the North Greenland Ice stream may be very high. The methodology used here can be extended to other ice core locations in Greenland and Antarctica.

How to cite: Richards, D., Pegler, S., and Piazolo, S.: Numerical modelling of ice stream fabrics: Implications for recrystallization processes and basal slip conditions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3743, https://doi.org/10.5194/egusphere-egu22-3743, 2022.

14:26–14:33
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EGU22-5430
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ECS
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Presentation form not yet defined
On modelling of calving front positions at KNS, southwest Greenland
(withdrawn)
Julia Christmann, Erik Loebel, Martin Rückamp, and Angelika Humbert
14:33–14:40
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EGU22-896
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ECS
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On-site presentation
Guy Moss, Vjeran Višnjević, Cornelius Schröder, Jakob Macke, and Reinhard Drews

Mass loss from the Antarctic ice sheet is dominated by the integrity of the ice shelves that buttress it. The evolution and stability of ice shelves is dependent on a variety of parameters that cannot be directly observed, such as basal melt and ice rheology. Constraining these parameters is of great importance in making predictions of the future changes in ice shelves that have a quantifiable uncertainty. This inference task is difficult in practice as the number of unknown parameters is large, observations are often sparse, and the computational cost of ice flow models is high.

We aim to develop a framework for inferring joint distributions of mass balance and rheological parameters of ice shelves from observations such as ice geometry, surface velocities, and radar isochrones. Here, we begin by inferring a posterior distribution over basal melt parameters in along-flow sections of synthetic and real world ice shelves (Roi Baudouin). We use the technique of simulation-based inference (SBI), a machine learning framework for performing Bayesian inference when the likelihood function is intractable. The inference procedure relies on the availability of a simulator to model the dynamics of the ice shelves. For this we use the Shallow Shelf Approximation (SSA) implemented in the Python library Icepack.  First, we show that by combining these two tools we can recover the underlying parameters of synthetic 2D data with meaningful uncertainty estimates. In a second step, we apply our method to real observations and get estimates for the basal melt rates which are coherent with the data when running the forward model over a centennial timescale.



How to cite: Moss, G., Višnjević, V., Schröder, C., Macke, J., and Drews, R.: Uncertainty quantification for melt rate parameters in ice shelves using simulation-based inference, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-896, https://doi.org/10.5194/egusphere-egu22-896, 2022.

14:40–14:47
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EGU22-2535
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ECS
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Presentation form not yet defined
Julien Bodart, Robert Bingham, Duncan Young, Donald Blankenship, and David Vaughan

Modelling the past and future evolution of the West Antarctic Ice Sheet (WAIS) to climate and ocean forcing is challenged by the availability and quality of observed palaeo boundary conditions. Aside from point-based geochronological measurements, the only available proxy to query past ice-sheet processes on large spatial scales is Internal Reflecting Horizons (IRHs) as sounded by ice-penetrating radar. When isochronal, IRHs can be used to determine palaeo-accumulation rates and patterns, as previously demonstrated using shallow, centennially dated layers. Whilst similar efforts using deeper IRHs have previously been conducted over the East Antarctic Plateau where ice-flow is slow and ice thickness has been stable through time, much less is known of millennial-scale accumulation rates over the West Antarctic plateau due to challenging ice dynamical conditions in the downstream section of the ice sheet. Using deep and spatially extensive ice-core dated IRHs over Pine Island and Thwaites glaciers and a local layer approximation model, we quantify Holocene accumulation rates over the slow-flowing parts of these sensitive catchments. The results from the one-dimensional model are also compared with modern accumulation rates from observational and modelled datasets to investigate changes in accumulation rates and patterns between the Holocene and the present. The outcome of this work is that together with present and centennial-scale accumulation rates, our results can help determine whether a trend in accumulation rates exists between the Holocene and the present and thus test to what extent these glaciers are controlled by ice dynamics rather than changes in accumulation rates.

How to cite: Bodart, J., Bingham, R., Young, D., Blankenship, D., and Vaughan, D.: Quantifying Holocene Accumulation Rates from Ice-Core Dated Internal Layers from Ice-Penetrating Radar Data over the West Antarctic Ice Sheet, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2535, https://doi.org/10.5194/egusphere-egu22-2535, 2022.

Coffee break
Chairpersons: Elisa Mantelli, Johannes Sutter
15:10–15:17
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EGU22-4027
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ECS
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On-site presentation
Michael Wolovick, Lea-Sophie Höyns, Thomas Kleiner, Niklas Nickel, Veit Helm, and Angelika Humbert

Lubrication by subglacial water or saturated subglacial sediments is crucial to controlling the movement of fast-flowing outlet glaciers and ice streams.  However, the subglacial environment is difficult to observe directly.  Here, we combine inverse modeling with ice-penetrating radar observations to characterize the ice sheet bed in the Filchner-Ronne sector of Antarctica, with a specific focus on the Recovery Glacier catchment.  First, we use the Ice Sheet System Model (ISSM; Larour et al., 2012) to assimilate satellite observations of ice sheet surface velocity (Mouginot et al., 2019) in order to solve for basal drag and ice rheology across the Filchner-Ronne sector of Antarctica.  Next, we compare these results with ice-penetrating radar observations sensitive to the presence of ponded water at the ice sheet base (Humbert et al., 2018; Langley et al., 2011), along with remotely sensed observations of active lakes (Smith et al., 2009) and putative large subglacial lakes inferred from the ice sheet surface slope (Bell et al., 2007).  We find that the main fast-flowing region of Recovery Glacier is mostly low-drag, with the exception of localized sticky spots and bands.  The boundary between rugged subglacial highlands and a deep subglacial basin near the onset of the ice stream is associated with a sharp reduction in basal drag, although surface velocity changes smoothly rather than abruptly across this transition.  An upstream shear margin, visible in satellite radar images of the ice surface, is associated with low basal drag.  The putative large lakes have low drag but are not strongly distinguished from their surroundings, and radar evidence for ponded subglacial water within them is weak.  The active lakes identified from satellite altimetry are similarly situated in areas of low basal drag, but have limited radar evidence for ponded subglacial water.  An L-curve analysis indicates that our inverse model results are robust against changes in regularization, yet the radar-identified lake candidates do not have a clear relationship with low-drag areas in the fast-flowing ice stream.  We conclude that the deep-bedded regions of Recovery Glacier are underlain by saturated subglacial sediments, but classic ponded subglacial lakes are much more rare.  Isolated sticky spots and bands within the ice stream are either due to protrusions of bedrock out of the sediments or to localized areas of frozen and/or compacted sediments.

How to cite: Wolovick, M., Höyns, L.-S., Kleiner, T., Nickel, N., Helm, V., and Humbert, A.: Basal Properties of the Filchner-Ronne Sector of Antarctica from Inverse Modeling and Comparison with Ice-Penetrating Radar Data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4027, https://doi.org/10.5194/egusphere-egu22-4027, 2022.

15:17–15:24
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EGU22-5425
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On-site presentation
Andreas Born, Alexander Robinson, and Alexios Theofilopoulos

Radar reflections from the interior of the Greenland ice sheet contain a comprehensive archive of past accumulation rates, ice dynamics, and basal melting. Combining these data with dynamic ice sheet models may greatly aid model calibration, improve past and future sea level estimates, and enable insights into past ice sheet dynamics that neither models nor data could achieve alone.

In this study, we present the first three-dimensional ice sheet model that explicitly simulates the Greenland englacial stratigraphy. Individual layers of accumulation are represented on a grid whose vertical axis is time so that they do not exchange mass with each other as the flow of ice deforms them. This isochronal advection scheme does not influence the ice dynamics and only requires modest input data from a host thermomechanical ice sheet model.

Using an ensemble of simulations, we show that direct comparison with the dated radiostratigraphy data yields notably more accurate results than calibrating simulations based on total ice thickness. We show that the isochronal scheme produces a more reliable simulation of the englacial age profile than Eulerian age tracers. Lastly, we outline how the isochronal model can be linearized as a foundation for inverse modeling and data assimilation.

How to cite: Born, A., Robinson, A., and Theofilopoulos, A.: Modeling the Greenland englacial stratigraphy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5425, https://doi.org/10.5194/egusphere-egu22-5425, 2022.

15:24–15:31
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EGU22-9143
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ECS
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On-site presentation
Rebecca Sanderson, Neil Ross, Louise Callard, Kate Winter, Felipe Napoleoni, Robert Bingham, and Tom Jordan

The analysis of englacial layers using ice penetrating radar enables the characterisation and reconstruction of current and past ice sheet flow. To date, little research has been undertaken on the ice flow and englacial stratigraphy of the upper catchment of the Lambert Glacier. The Lambert Glacier catchment is one of the largest in East Antarctica, discharging ~16% of East Antarctica’s ice. Quantitative analysis of the continuity of englacial stratigraphy and ice flow has the potential to provide insight into the present-day and past flow regimes of the upper catchment of Lambert Glacier. Radar data from the British Antarctic Survey Antarctica’s Gamburtsev Province Project North (AGAP-N) aerogeophysical survey was analysed using the Internal Layer Continuity Index (ILCI). This approach identified, and characterised, a range of englacial structures and stratigraphy, including buckled layers in areas of increased ice velocity (>20ma-1) and continuous layering across subglacial highlands with low ice velocity adjacent to the central Lambert Glacier trunk. Overall, the analysis is consistent with the present-day ice-flow velocity field and long-term stability of ice flow across the Lambert catchment. However, disrupted layer geometry at the onset of the Lambert Glacier suggests a past shift in the position of the onset of ice flow. These results have implications for the future evolution of this major ice flow catchment, and East Antarctica, under a changing climate. The ILCI method represents a valuable tool for rapidly characterising englacial stratigraphy, and the study demonstrates the transferability of the method across Antarctica. The use of quantitative tools such as ILCI for the analysis of large radar datasets will be critical for projects such as AntArchitecture (https://www.scar.org/science/antarchitecture/home/) which aims to investigate the long-term stability of the Antarctic ice sheets directly from the internal architecture of the ice sheet.

How to cite: Sanderson, R., Ross, N., Callard, L., Winter, K., Napoleoni, F., Bingham, R., and Jordan, T.: Assessing the continuity of englacial layers across the Lambert Glacier catchment., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9143, https://doi.org/10.5194/egusphere-egu22-9143, 2022.

15:31–15:38
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EGU22-11310
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ECS
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Presentation form not yet defined
Elisa Mantelli, Marnie Bryant, Helene Seroussi, Ludovic Raess, Davide Castelletti, Dustin Schroeder, Jenny Suckale, and Martin Siegert

Transitions from basal no slip to basal sliding are a common feature of ice sheets, yet one that has remained difficult to observe. In this study we leverage recent advances in the processing of radar sounding data to study these transitions through their signature in englacial layers. Englacial layers encode information about strain and velocity, and the relation between their geometry and the onset of basal sliding has been demonstrated in ice flow models (the so-called "Weertman effect"). Here we leverage this relation to test the long-standing hypothesis that sliding onset takes the form of an abrupt no slip/sliding transition. By comparing the modeled signature of an abrupt sliding onset in englacial layer slopes against slope observations from the onset region of a West Antarctic ice stream (Institute Ice Stream), we conclude that observed layer geometry does not support an abrupt no slip/sliding transition. Our findings instead suggest a much smoother sliding onset, as would be consistent with temperature-dependent friction between ice and bed. Direct measurements of basal temperature at the catchment scale would allow us to confirm this hypothesis.

How to cite: Mantelli, E., Bryant, M., Seroussi, H., Raess, L., Castelletti, D., Schroeder, D., Suckale, J., and Siegert, M.: Layer geometry as a constraint on the physics of sliding onset, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11310, https://doi.org/10.5194/egusphere-egu22-11310, 2022.

15:38–15:45
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EGU22-13501
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Presentation form not yet defined
Eliza Dawson, Dustin Schroeder, Winni Chu, Elisa Mantelli, and Hélène Seroussi

Contemporary mass loss from the Antarctic ice sheet primarily originates from the discharge of
marine-terminating glaciers and ice streams. The rate of mass loss is influenced by warming ocean
and atmospheric conditions, which lead to subsequent thinning or disintegration of ice shelves and
increased outflow of upstream grounded ice. It is currently unclear how the basal thermal state of
grounded ice could evolve in the future - for example as a result of accelerated ice flow or changes
in the ice sheet geometry - but a change in the basal thermal state could impact rates of mass loss
from Antarctica. Here, we use a combination of numerical simulations and ice-penetrating radar
analysis to investigate the influence of basal thawing on 100yr simulations of the Antarctic ice
sheet’s evolution. Using the Ice-sheet and Sea-level System Model, we find that thawing patches
of frozen bed near the ice sheet margin could drive mass loss extending into the continental
interior, with the highest rates of loss coming from the George V - Adélie - Wilkes Land coast and
the Enderby - Kemp Land regions of East Antarctica. This suggests that the thawing of localized
frozen bed patches is sufficient to cause these East Antarctic regions to transition to an unstable
mass loss regime. We constrain model estimates of the basal thermal state using ice-penetrating
radar surveys and analyze radar characteristics including bed reflectivity and attenuation. In
combination, our work identifies critical regions of Antarctica where the ice-bed interface could
be close to thawing and where basal thaw could most impact mass loss.

How to cite: Dawson, E., Schroeder, D., Chu, W., Mantelli, E., and Seroussi, H.: Investigating basal thaw as a driver of mass loss from the Antarctic ice sheet, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13501, https://doi.org/10.5194/egusphere-egu22-13501, 2022.

15:45–15:52
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EGU22-8605
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On-site presentation
Martin Wearing, Daniel Goldberg, Christine Dow, Anna Hogg, and Noel Gourmelen

Meltwater forms at the base of the Antarctic Ice Sheet due to geothermal heat flux (GHF) and basal frictional dissipation. Despite the relatively small volume, this meltwater has a profound effect on ice-sheet stability, controlling the dynamics of the ice sheet and the interaction of the ice sheet with the ocean. However, observations of subglacial melting and hydrology in Antarctica are limited. Here we use numerical modelling to assess subglacial melt rates and hydrology beneath the Antarctic Ice Sheet. Our case study, focused on the Amery Ice Shelf catchment, shows that total subglacial melting in the catchment is 6.5 Gt yr-1, over 50% larger than previous estimates. Uncertainty in estimates of GHF leads to a variation in total melt of ±7%. The meltwater provides an extra 8% flux of freshwater to the ocean in addition to contributions from iceberg calving and melting of the ice shelf. GHF and basal dissipation contribute equally to the total melt rate, but basal dissipation is an order of magnitude larger beneath ice streams. Remote-sensing observations, from CryoSat-2, indicating active subglacial lakes and ice-shelf basal melting constrain subglacial hydrology modelling. We observe a network of subglacial channels that link subglacial lakes and trigger isolated areas of sub-ice-shelf melting close to the grounding line. Building upon this Amery case study, we expand our analysis to quantify subglacial melt rates and hydrology beneath the entire Antarctic Ice Sheet.

How to cite: Wearing, M., Goldberg, D., Dow, C., Hogg, A., and Gourmelen, N.: Coupling modelling and satellite observations to constrain subglacial melt rates and hydrology, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8605, https://doi.org/10.5194/egusphere-egu22-8605, 2022.