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.

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.

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.

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 of regional 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.

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.

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.

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.

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.