CR2.9

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.

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.

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.

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.

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.

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.

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.

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.