This interdisciplinary session brings together modellers and observationalists to present results and exchange knowledge and experience in the use of data assimilation in the cryospheric sciences such as inverse methods, geostatistics and machine learning. In numerous research fields it is now possible to not only 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 improve 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 the 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 methodological backgrounds - from satellite observations to deep-looking geophysical methods and advancements in numerical techniques - and research topics including permafrost, sea ice and snow to glaciers and ice sheets, covering static system characterisation as well as transient processes.
Permafrost, the frozen layer beneath a freezing and thawing active layer, is an impermeable frozen soil that persists for multiple years. The gradual thawing of permafrost and thickening of the active layer allows a glimpse into the evolution of the hydraulic processes that shape the periglacial landscape. One question in understanding the governing mechanics within the rapidly evolving periglacial landscape is how water retains within or segregates through the active layer to eventually feed rivers.
In this exploratory study, we analyze data from multiple periglacial hydraulic catchments over time and characterize their hydraulic response rate to stressors. We test whether deconvolution and demixing of noisy time series can isolate precipitation from thawing permafrost signals in river discharge. We use the Ensemble Rainfall-Runoff (ERRA) script, which is effective in inferring nonstationary and nonlinear responses to precipitation using Runoff Response Distribution (RRD), to further test temperature signatures. Using this tool, we measure the RRD for the same catchments both over the years and over the summer months. We hypothesize that an increase in active layer thickness over years and over summer months will delay the RRD due to an increase in water storage.
By analyzing the parameters that change the RRD of periglacial systems with time, soil moisture content, average seasonal and yearly temperatures, and precipitation, we can begin a systematic understanding of how the active layer modulates hydraulic responses and how the responses may be different from other hydraulic systems.
How to cite:
Culha, C. and Kirchner, J.: Characterizing melt water properties in the periglacial active layer through seasonal and yearly variations in catchment hydrology., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4291, https://doi.org/10.5194/egusphere-egu23-4291, 2023.
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Baptiste Vandecrux, Robert S. Fausto, Jason E. Box, Federico Covi, Regine Hock, Asa Rennermalm, Achim Heilig, Jakob Abermann, Dirk Van As, Anja Løkkegaard, Xavier Fettweis, Paul C. J. P. Smeets, Peter Kuipers Munneke, Michiel Van Den Broeke, Max Brils, Peter L. Langen, Ruth Mottram, and Andreas P. Ahlstrøm
The Greenland ice sheet mass loss is one of the main sources of contemporary sea-level rise. The mass loss is primarily caused by surface melt and the resulting runoff. During the melt season, the ice sheet’s surface receives energy from sunlight absorption and sensible heating, which subsequently heats the subsurface snow and ice. The energy from the previous melt season can also enhance melting in the following summer as less heating is required to bring the snow and ice to the melting point. Subsurface temperatures are therefore both a result and a driver of the timing and magnitude of surface melt on the ice sheet. We present a dataset of more than 3900 measurements of ice, snow and firn temperature at 10 m depth across the Greenland ice sheet spanning the years from 1912 to 2022. We construct an artificial neural network (ANN) model that takes as input the ERA5 reanalysis monthly near-surface air temperature and snowfall for the 1954-2022 period and train it on our compilation of observed 10-meter temperature. We use our dataset and the ANN to evaluate three broadly used regional climate models (RACMO, MAR and HIRHAM). Our ANN model provides an unprecedented and observation-based description of the recent warming of the ice sheet’s near-surface and our evaluation of the three climate models highlights future development for the models. Overall, these findings improve our understanding of the ice sheet’s response to recent atmospheric warming and will help reduce uncertainties of ice sheet surface mass balance estimates.
How to cite:
Vandecrux, B., Fausto, R. S., Box, J. E., Covi, F., Hock, R., Rennermalm, A., Heilig, A., Abermann, J., Van As, D., Løkkegaard, A., Fettweis, X., Smeets, P. C. J. P., Kuipers Munneke, P., Van Den Broeke, M., Brils, M., Langen, P. L., Mottram, R., and Ahlstrøm, A. P.: Historical snow and ice temperature compilation documents the recent warming of the Greenland ice sheet, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9080, https://doi.org/10.5194/egusphere-egu23-9080, 2023.
Tatiana Smirnova, Anton Kliewer, Siwei He, and Stan Benjamin
RUC land surface model (LSM) was designed for short-range weather predictions with an emphasis on severe weather. The model has been operational at NCEP since 1998. Currently it is utilized in the operational WRF-based Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) regional models. Being available to the world WRF community, RUC LSM is also used as a land-surface component in operational weather prediction models in Austria, New Zealand and Switzerland.
At present time, RUC LSM is being tested in the regional application of the UFS-based Rapid Refresh FV3 Standalone (RRFS) model to replace operational RAP and HRRR at NCEP.
RUC LSM has improved and matured over the years. The unique feature of this land-surface model is continuous evolution of soil/snow states within moderately coupled land data assimilation (MCLDA). To avoid possible drifts, this feature requires high skill from RUC LSM as well as accurate atmospheric forcing. Continuous snow cycling includes the following snow state variables: snow cover fraction, snow depth, snow water equivalent and snow temperature. To avoid possible inaccuracies in the location of cycled snow on the ground, snow depth is corrected daily using 4-km IMS snow cover information. Work is also underway to further improve RUC snow model for better surface predictions over snow-covered areas. RUC snow model uses “mosaic” approach to account for subgrid variability of snow cover. Within this approach, snow-covered and snow-free portions of the grid cells are treated separately in the solution of energy and moisture budgets. Thus, snow cover fraction becomes a critical parameter, and modifications to its computation have been developed and tested in the RRFS retrospective experiments. Results from these validation experiments will be presented at the meeting.
How to cite:
Smirnova, T., Kliewer, A., He, S., and Benjamin, S.: Advancements in RUC Snow Model for Implementation in the Regional Application of the Unified Forecasting System (UFS), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8775, https://doi.org/10.5194/egusphere-egu23-8775, 2023.
Jeremy Carter, Erick Chacón Montalván, and Amber Leeson
Regional Climate Models (RCM) are the primary source of climate data available for impact studies over Antarctica. These climate-models experience significant, large-scale biases over Antarctica for variables such as snowfall, surface temperature and melt. Correcting for these biases is desirable for impact models being driven by meteorological data that aim to produce optimal estimates of for example surface run-off and ice discharge. Typical approaches to bias correction often neglect the handling of uncertainties in parameter estimates and don’t account for the different supports of climate-model and observed data. Here a fully Bayesian approach using latent Gaussian processes is proposed for bias correction, where parameter uncertainties are propagated through the model. Advantages of this approach are demonstrated by bias-correcting RCM output for near-surface air temperature over Antarctica.
How to cite:
Carter, J., Chacón Montalván, E., and Leeson, A.: Bias correction of climate models using observations over Antarctica., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14292, https://doi.org/10.5194/egusphere-egu23-14292, 2023.
Marie G. P. Cavitte, Hugues Goosse, Kenichi Matsuoka, Sarah Wauthy, Rahul Dey, Vikram Goel, Jean-Louis Tison, Brice Van Liefferinge, and Thamban Meloth
Ice cores remain the highest resolution proxy for measuring past surface mass balance (SMB) that can be used for model-data comparison. However, there is a clear difference in the spatial resolution of the ice cores, with a surface sample on the order of cm2, and the spatial resolution of models, with at best a surface footprint on the order of a few km2. Comparing ice core SMB records and model SMB outputs directly is therefore not a one-to-one comparison. In addition, it is well known that ice cores, as point measurements, sample very local SMB conditions which can be affected by local wind redistribution of the SMB at the surface.
We set out to answer the question: how representative are ice-cores ofregional SMB? For this, we use several ground-penetrating radar (GPR) surveys in East Antarctica, which have co-located ice core drill sites. Most of our sites share a relatively similar climatology, as they are all coastal ice promontories/rises along the Dronning Maud Land coast, with the exception of the Dome Fuji survey on the high plateau in the interior of the continent.
We will show that the comparison of the SMB signals of the GPR and the ice core records allows us to estimate the spatial footprint of the ice cores, and that this spatial footprint varies widely from site to site. We will provide a summary of the spatial and temporal characteristics for each location.
How to cite:
Cavitte, M. G. P., Goosse, H., Matsuoka, K., Wauthy, S., Dey, R., Goel, V., Tison, J.-L., Van Liefferinge, B., and Meloth, T.: What to watch out for when assimilating ice-cores as regional SMB proxies?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-792, https://doi.org/10.5194/egusphere-egu23-792, 2023.
The stability of the Greenland ice sheet is poorly constrained even for benchmark periods such as the mid-Holocene or Eemian. Since ice stratigraphy holds a record of both surface mass balance (SMB) and ice dynamics, dated radiostratigraphy offer a potential route to improved reconstructions. Here we explicitly simulate isochrones and employ inverse methods to optimize the solution. The Englacial Layer Simulation Architecture (ELSA) coupled with a thermomechanical ice sheet model computes the isochrones or ice layers, which enable the direct comparison with the radiostratigraphy data. The accumulation rates force ELSA, and are adjusted until the model reproduces the observations within their uncertainties. We deploy the Ensemble Kalman Smoother for the data assimilation. This results in not only the reconstruction of the SMB; an optimized simulation of the ice sheet is obtained by solving the corresponding forward problem. Hence, the contribution to sea level change by Greenland over the same period can also be constrained.
Here we present our initial approach and preliminary results of SMB reconstruction. Future plans and expansions of the work are also presented, involving the study of several model parameters such as basal traction.
How to cite:
Voigt, P. I. and Born, A.: Reconstructing accumulation rates of the Greenland ice sheet using dated radiostratigraphy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15374, https://doi.org/10.5194/egusphere-egu23-15374, 2023.
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Guy Moss, Vjeran Višnjević, Cornelius Schröder, Jakob Macke, and Reinhard Drews
The ice shelves buttressing the Antarctic ice sheet determine its stability. Over half of all mass loss in Antarctica occurs due to ice melting at the water-ice boundary at the base of ice shelves. Different contemporary methods of estimating the spatial distribution of the melting rates do not produce consistent results, and provide no information about decadal to centennial timescales. We explore a new method to infer the spatial distribution of the basal mass balance (BMB) using the internal stratigraphy which may contain additional information not present in other sources such as ice thickness and surface velocities alone. The method estimates the Bayesian posterior distribution of the BMB, and provides us with a principled measure of uncertainty in our estimates.
Our inference procedure is based on simulation-based inference (SBI) [1], a novel machine learning inference method. SBI utilizes artificial neural networks to approximate probability distributions which characterize those parameters that yield data-compatible simulations, without the need of an explicit likelihood function. We demonstrate the validity of our method on a synthetic ice shelf example, and then apply it to Ekström ice shelf, East Antarctica, where we have radar measurements of the internal stratigraphy. The inference procedure relies on a simulator of the dynamics of the ice shelves. For this we use the Shallow Shelf Approximation (SSA) implemented in the Python library Icepack [2], and a time-discretized layer tracing scheme [3]. These detailed simulations, along with available stratigraphic data and the SBI methodology, allows us to compute a spatially-varying posterior distribution of the melting rate. This distribution corroborates existing estimates and extends upon them by quantifying the uncertainty in our inference. This uncertainty should be incorporated in future forecasting of ice shelf dynamics and stability analysis.
[1] Lueckmann et al.: Benchmarking simulation-based inference (2020).
[2] Shapero et al.: icepack: a new glacier flow modeling package in Python, version 1.0. (2021).
[3] Born: Tracer transport in an isochronal ice-sheet model (2017).
How to cite:
Moss, G., Višnjević, V., Schröder, C., Macke, J., and Drews, R.: Determining Basal Mass Balance of Ice Shelves Using Simulation-Based Inference, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6900, https://doi.org/10.5194/egusphere-egu23-6900, 2023.
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Olaf Eisen, Steven Franke, Paul D. Bons, Julien Westhoff, Ilka Weikusat, Tobias Binder, Kyra Streng, Daniel Steinhage, Veit Helm, John D. Paden, Graeme Eagles, and Daniela Jansen
Reliable knowledge of ice discharge dynamics for the Greenland ice sheet via its ice streams is essential if we are to understand its stability under future climate scenarios as well as their dynamics in the past, especially when using numerical models for diagnosis and prediction. Currently active ice streams in Greenland have been well mapped using remote-sensing data while past ice-stream paths in what are now deglaciated regions can be reconstructed from the landforms they left behind. However, little is known about possible former and now defunct ice streams in areas still covered by ice. Here we use radio-echo sounding data to decipher the regional ice-flow history of the northeastern Greenland ice sheet on the basis of its internal stratigraphy. By creating a three-dimensional reconstruction of time-equivalent horizons, we map folds deep below the surface that we then attribute to the deformation caused by now-extinct ice streams. We propose that locally this ancient ice-!ow regime was much more focused and reached much farther inland than today’s and was deactivated when the main drainage system was reconfigured and relocated southwards. The insight that major ice streams in Greenland might start, shift or abruptly disappear will affect our approaches to understanding and modelling the past or future response of Earth’s ice sheets to global warming. Such behaviour has to be sufficiently reproduced by numerical models operating on the mid- to longer-term timescales to be considered adequate physical representations of the naturally occuring dynamic behaviour of ice streams.
How to cite:
Eisen, O., Franke, S., Bons, P. D., Westhoff, J., Weikusat, I., Binder, T., Streng, K., Steinhage, D., Helm, V., Paden, J. D., Eagles, G., and Jansen, D.: Greenland ice-stream dynamics: short-lived and agile?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6770, https://doi.org/10.5194/egusphere-egu23-6770, 2023.
Fabien Gillet-Chaulet, Eliot Jager, and Mathieu Morlighem
While being one of the most important variables for predicting the future of the ice sheets, observations of ice thickness are only available along flight tracks, separated by a few to a few tens of kilometres. For many applications, these observations need to be interpolated on grids at a much higher resolution than the actual average spacing between tracks.
The mass conservation method is an inverse method that combines the sparse ice thickness data with high resolution surface velocity observations to obtain a high-resolution map of ice thickness that conserves mass and minimizes the departure from observations. As with any inverse method, the problem is ill-posed and requires some regularisation. The classical approach is to use a Tikhonov regularisation that penalizes the spatial derivatives of the ice thickness and therefore favours smooth solutions with implicit spatial correlation structures. In a Bayesian framework, regularization can be seen as an implicit assumption for the prior probability distribution of the inverted parameter. Other popular geostatistical interpolation algorithms, such as kriging, usually require to parameterize the spatial correlation of the interpolated field using standard correlation functions (e.g., gaussian, exponential, Matèrn).
Here we replace the Tikhonov regularisation term in the mass conservation method by a term that penalises the departure from a prior, where the error statistics are parametrized with the same standard correlation functions. This makes the regularisation independent from grid spacing and regularisation weights do not need to be adjusted. We present and discuss the sensitivity of the mass conservation method to the regularisation scheme using a suite of synthetic and “true” bed from deglaciated areas and show that prescribing the correct regularisation always provides the most accurate solution.
How to cite:
Gillet-Chaulet, F., Jager, E., and Morlighem, M.: Sensitivity of the mass conservation method to the regularisation scheme, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5504, https://doi.org/10.5194/egusphere-egu23-5504, 2023.
Zhuo Wang, Olaf Eisen, Ailsa Chung, Daniel Steinhage, Frédéric Parrenin, and Johannes Freitag
The Dome Fuji (DF) region in Antarctica is a potential site for holding an ice record older than one million years. Here, we combine the internal airborne radar stratigraphy with a 1-D inverse model to reconstruct the age field of ice in the DF region. As part of the Beyond EPICA - Oldest Ice reconnaissance (OIR), the region around DF was surveyed with a total of 19000 km of radar lines in the 2016/17 Antarctic summer. Internal stratigraphy in this region has now been traced. Through these tracked radar isochrones, we transfer the age-depth scale from DF ice core to the adjacent 500 km2 region. A 1-D inverse model has been applied at each point of the survey to extend the age estimates to deeper regions of the ice sheet where no direct or continuous link of internal stratigraphy to the ice cores is possible, and to construct basal thermal state and accumulation rates. Through the reliability index of each model, we can evaluate the reliability of the 1-D assumption. Mapped age of basal ice and age density imply there might exist promising sites with ice older than 1.5 million years in the DF region. Moreover, the deduced basal state, i.e., melting rates and stagnant ice provide constraints for finding old-ice sites with a cold base. The accumulation rate ranges from 0.014 to 0.038 m a-1 (in ice equivalent) in the DF region, which is also an important criterion for potential old ice.
How to cite:
Wang, Z., Eisen, O., Chung, A., Steinhage, D., Parrenin, F., and Freitag, J.: Mapping stagnant ice and age in the Dome Fuji region, Antarctica, by combining radar internal layer stratigraphy and flow modeling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-787, https://doi.org/10.5194/egusphere-egu23-787, 2023.
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Elisa Mantelli, Reinhard Drews, Olaf Eisen, Daniel Farinotti, Martin Luethi, Laurent Mingo, Dustin Schroeder, and Andreas Vieli
Fast ice stream flow at speeds of hundreds to thousands of meters per year is sustained by sliding at the ice sheet base, whereas slow flow outside of ice streams is characterized by limited-to-no basal sliding. In this sense, the transition from no sliding to significant sliding exerts a key control on ice stream flow. The detailed physical processes that enable the onset of basal sliding are somewhat debated, but laboratory experiments, recent theoretical work, and a handful of direct observations support the notion of sliding initiating below the melting point as a result of regelation and premelting.
In this contribution we describe a recently funded glacier-scale field experiment that has been designed to advance the understanding of sliding onset physics by testing the hypothesis that sliding starts below the melting point. The experiment will take place at the Grenzgletscher (Swiss Alps), which is known to have a cold-based accumulation region and a temperate-based ablation region. Our work will involve extensive surface geophysics (radio echo sounding, terrestrial radar interferometry, radar thermal tomography) aimed at identifying the sliding onset region. This work will guide the site selection for a subsequent borehole study of englacial deformation that is meant illuminate the relation between sliding velocity and basal temperature. The borehole work will allow us to test systematically the hypothesis that sliding starts below the melting point through an extended region of temperature-dependent sliding, and possibly to advance the formulation of temperature-dependent friction laws that are used to describe the onset of sliding in ice flow models.
The focus of this contribution will be specifically on the experimental design - how it is informed by existing theory and observations, and how it will support theoretical and ice flow modeling advances, at the glacier scale and beyond.
How to cite:
Mantelli, E., Drews, R., Eisen, O., Farinotti, D., Luethi, M., Mingo, L., Schroeder, D., and Vieli, A.: At the bottom of ice streams: unraveling the physics of sliding onset through a glacier-scale field experiment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12553, https://doi.org/10.5194/egusphere-egu23-12553, 2023.
Tun Jan Young, Carlos Martin, Thomas Jordan, Ole Zeising, Olaf Eisen, Poul Christoffersen, David Lilien, and Nicholas Rathmann
Glaciers and ice streams account for the majority of ice mass discharge to the ocean from the Antarctic Ice Sheet, and are bounded by intense bands of shear that separate fast-flowing from slow or stagnant ice, called shear margins. The anisotropy of glacier ice (i.e. a preferred crystal orientation) stemming from high rates of shear at these margins can greatly facilitate fast streaming ice flow, however it is still poorly understood due to a lack of in-situ measurements. If anisotropy is incorporated into numerical ice sheet models at all, it is usually as a simple scalar enhancement factor that represents the "flow law" that governs the model's rheology. Ground-based and airborne radar observations along two transects fully crossing the Eastern Shear Margin of Thwaites Glacier reveal rapid development of highly anisotropic fabric tightly concentrated around a lateral maximum in surface shear strain. These measurements of fabric strength at the centre of the shear margin are indicative of a horizontal pole configuration, which potentially represents ice that is “softened” to shearing in some directions and hardened in others. The resulting flow enhancement revealed by our results suggest that the viscosity of ice is highly variable and regime-dependent, and supports the importance of considering anisotropic flow laws to model the rheology of ice sheets.
How to cite:
Young, T. J., Martin, C., Jordan, T., Zeising, O., Eisen, O., Christoffersen, P., Lilien, D., and Rathmann, N.: Radar-derived ice fabric anisotropy and implications on flow enhancement along the Thwaites Glacier Eastern Shear Margin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2178, https://doi.org/10.5194/egusphere-egu23-2178, 2023.
Leah Sophie Muhle, Guy Moss, A. Clara J. Henry, and Reinhard Drews
Projections of the future development of the Antarctic Ice Sheet still exhibit a large degree of uncertainty due to difficulties in constraining parameters of ice-flow models such as basal boundary conditions. Deriving better estimates of these parameters from radargrams could greatly improve model accuracy, but integration of inferred radar attributes into ice-flow models is not yet widespread.
Here, we develop a radar forward modeling framework that is geared to train a machine learning workflow (likely simulation-based inference) to extract radar attributes such as the internal stratigraphy and basal boundary conditions (e.g., frozen vs. wet) from radar data. The workflow starts with ice-dynamic forward models predicting physically sound stratigraphies and internal/basal temperatures for synthetic flow settings using shallow ice, shallow shelf and higher order ice-flow models. This is then used as input to the radar simulator (here gprMax), which calculates the radar image produced by such a stratigraphy. To do so, we match the synthetic permittivities of the modeled stratigraphy with statistical properties known from ice-core logging data and prescribe temperature dependent attenuation via an Arrhenius relation. gprMax is optimized for acceleration using GPUs which can be efficiently employed when solving the FDTD discretized Maxwell equations. Currently, 200 m wide and 500 m deep sections can be simulated on a single NVIDIA GeForce RTX 2070 Super graphics card within 390 minutes. The runtime can be substantially improved in a HPC environment. In order to obtain radar simulations comparable with observations, we also add system specific noise and contributions from volume scattering with variable surface roughness. Here, we focus on 50 MHz pulse radar for which we have many observational counterparts. However, the workflow is designed to encompass multiple ice-dynamic settings and different radar frequencies.
The application of physical forward models will result in physically meaningful radargrams which are indistinguishable from observations. This provides a tool to create datasets for training machine learning workflows for inference without the limitations of hand-labeled data.
How to cite:
Muhle, L. S., Moss, G., Henry, A. C. J., and Drews, R.: Radar forward modelling as a precursor for statistical inference, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12495, https://doi.org/10.5194/egusphere-egu23-12495, 2023.
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