Over the past decade, the availability of new low-frequency microwave spaceborne data has provided key parameters of the cryosphere and polar ocean that can be assimilated into Earth System Models, enhancing our understanding of fundamental processes. Building on these findings, new initiatives have emerged to explore the potential of using even lower frequencies (with the current lower limit being 1.4 GHz). These lower frequencies can penetrate deeper into ice and have shown greater sensitivity to sea surface salinity in cold waters. Airborne surveys conducted in Greenland and Antarctica have demonstrated the potential of low-frequency wideband radiometers in monitoring polar regions, offering unprecedented capabilities compared to existing and planned spaceborne satellites. The ESA Earth Explorer 12 CryoRad mission candidate aims to fully demonstrate these capabilities and produce key scientific data for advancing cryosphere studies. CryoRad consists of a single satellite equipped with a broadband low-frequency microwave radiometer operating in the range 0.4 to 2 GHz with continuous frequency scanning with frequent revisit and a complete coverage of polar regions. The three main mission objectives are: (i) Better assess the mass balance and stability of ice sheets, by bridging the observation gap for ice sheet  temperature profiles of Antarctic and Greenland ice sheets, extending from surface to base, a dataset previously available only through limited borehole observations or models; (ii) Better assess the freshwater cycle and water mass formation at high latitudes, by bridging the observation gap for sea surface salinity in cold waters enhancing the uncertainty by at least a factor of 2 compared with existing L-band measurements; (iii) Investigate sea ice dynamics and salinity exchange processes in the Arctic and Antarctic, by bridging the observation gap of sea ice thickness in the range 0.5-1 m and deliver the first spaceborne observations of sea ice salinity. Scientific and industrial studies are currently on-going to improve the mission concept and to accurately design the products’ requirements and instrument parameters. The aim of the paper is to present the mission concept to the scientific community, discuss the methodologies for extracting geophysical parameters, and evaluate the potential impact of these new parameters on Earth System Models. 

How to cite: Brogioni, M., Macelloni, G., Leduc-Leballeur, M., Picard, G., Boutin, J., Quiquet, A., Kaleschke, L., Bertino, L., Lhermitte, S., Munck Solgaard, A., Høyer Svendsen, S., Jezek, K. C., Kontu, A., Rautiainen, K., Vergely, J.-L., Oliva, R., Onrubia, R., Zhou, Y., Tonboe, R., and Drusch, M.: A New Spaceborne Mission Concept for The Monitoring of the Cryosphere : CryoRAD, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10157, https://doi.org/10.5194/egusphere-egu25-10157, 2025.

Accurate estimates of iceberg populations, disintegration rates and iceberg movement are essential to understand ice sheet contributions to global sea level change, effects of freshwater inputs on ocean circulations and heat balances. Furthermore, there are operational imperatives to predict iceberg drift and fragmentation in order to ensure the safety and efficiency of polar shipping. The dynamics, persistence, fragmentation rates, melt rates and dispersal of icebergs are, however, poorly understood due to a lack of automated approaches for monitoring them.

We present an automated iceberg tracking approach that is capable of reconstructing iceberg paths, fragmentations and ultimately lineages through multiple generations based on satellite radar imagery. The method offers scope for the first time to relate iceberg fragments back to their original source computationally, which will allow scalable deployment and the development of improved predictive iceberg drift and disintegration models and a better understanding of contributions to nutrient and freshwater distributions. 

Tracking is developed using the Canadian Ice Island Drift, Deterioration and Detection (CI2D3) database. This contains manually-delineated observations of large tabular icebergs in the Canadian Arctic between 2008 and 2012 based on RADARSAT-1 and -2 imagery. Critically, CI2D3 documents the lineages of icebergs across fragmentation events and therefore provides a unique ground control dataset allowing evaluation of tracker performance.

Tracking of unchanging icebergs is achieved using a Bayesian tracking algorithm that makes linkages based upon a variety of geometric shape descriptors. Tracking across fragmentation events minimises Dynamic Time Warping distances between residual perimeter curves for candidate fragments and potential parents. This enables the matching of noisy, partial geometries and the automatic tessellation of fragments at one time step into the outline of their parent in a preceding observation irrespective of the intervening drift patterns. We evaluate tracker performance against bespoke metrics and those developed for cell tracking challenges that include mitotic division.

The system provides a generalisable geospatial tracking methodology based on object geometries that is applicable to other contexts and questions as well as a novel means of reconciling global invariances in geometries when conducting shape fingerprinting and matching.

How to cite: Evans, B., Fleming, A., Lowe, A., and Hosking, S.: Icebergs, Genealogy and Jigsaw Puzzles, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6110, https://doi.org/10.5194/egusphere-egu25-6110, 2025.

Seasonal snow, a critical resource for society and the climate system, provides water for billions, supports agriculture, clean energy, and tourism, and influences the global energy balance. However, accurately quantifying snow mass, particularly in mountainous regions, remains a challenge due to substantial observational and modelling limitations. As such, data assimilation (DA) offers a powerful tool for overcoming these limitations by integrating observations with physically-based models to improve estimates of thesnowpack. Previous snow DA studies have employed an Ensemble Kalman Filter (EnKF) to assimilate Sentinel-1 satellite-based snow depth retrievals, demonstrating improved accuracy in modelled snow depth, mass, and streamflow. In those studies, the observation uncertainty was assumed to be constant in space and time, which is not optimally making use of the observational information. Here, we present several advances in snow DA. Using an EnKF, we assimilate novel snow depth retrievals resulting from a machine learning product that uses Sentinel-1 backscatter observations, land cover, and topographic information over the European Alps. We also incorporate a state-dependent observation error, whereby the uncertainty of the assimilated snow depth observation varies in space and time with snow depth, better reflecting the variability of the snow depth retrieval uncertainty. The machine learning snow depth retrieval product is assimilated into the Noah-MP land surface model over the entire European Alps at 1 km for the years 2015-2023 and we evaluate modelled snow depth and snow water equivalent against independent in-situ measurements and modelled snow cover against satellite observations. This work demonstrates the benefits of machine learning based snow depth retrievals and variable observation errors in EnKF-based snow DA.

How to cite: Dunmire, D., Bechtold, M., Boeykens, L., and De Lannoy, G.: Advancing snow data assimilation with a variable, state-dependent observation uncertainty, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17352, https://doi.org/10.5194/egusphere-egu25-17352, 2025.

Surface mass balance (SMB) models are critical for understanding glacier evolution and projecting changes in response to climatic variations. This study presents a novel framework for calibrating SMB parameters using remotely sensed observations, incorporating the timing of data acquisition to improve accuracy and temporal relevance. The framework leverages the Ensemble Kalman Filter (EnKF), a robust data assimilation method, to iteratively refine model parameters based on incoming observations.

In our implementation, we decided on the Instructed Glacier Model (IGM) and embed it into the EnKF data assimilation approach. Before the transient ensemble simulations are started, a built-in stationary inversion is pursued to constrain ice-dynamic parameters and infer the basal topography. This stationary step relies on surface velocity, surface topography, and if available ice thickness measurements. For the transient evolution, a simple SMB model is calibrated using satellite-derived surface elevation changes. The calibration focuses on three primary parameters: the equilibrium line altitude (ELA) and two SMB elevation gradients for accumulation and ablation. This simplified SMB approach serves as a proof-of-concept, balancing simplicity with efficiency to showcase the effectiveness of the proposed method.

Initial results show that the method performs well for a synthetic glacier setup for which the target SMB is a-priori known. A sensitivity analysis highlights the importance of the key EnKF parameters. For real-world applications reasonable agreement is achieved with in-situ measurements - partially owing to the simple SMB approach. In summary, we are convinced that the approach could help improve our understanding of SMB processes, especially in regions with limited in-situ measurements.

How to cite: Herrmann, O., Groos, A., Tabone, I., Guillaume, J., and Fürst, J.: Calibrating Glacier Surface Mass Balance Using Remote Sensing and Ensemble Kalman Filter, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2868, https://doi.org/10.5194/egusphere-egu25-2868, 2025.

Changes in glacial loading of Mýrdalsjökull icecap impact the dynamic behavior of the subglacial volcano Katla. Here, we are quantifying the glacial changes since 2010 with a daily resolution. This will improve the understanding of Katla's response to both, long-term and seasonal changes.

To reconcile the temporally sparse but highly accurate data from spaceborne altimetry (ArcticDEM, Pléiades and IceSat2) with the higher temporal resolution surface mass balance products derived from the weather reanalysis CARRA, we apply a geographically weighted linear regression. This approach helps estimate biases in the reanalysis product and the divergence in glacial flow.

We demonstrate that residuals from this process are effective in identifying anomalies in glacial behavior, such as surges or geothermal activity.

How to cite: Liebsch, J., Aðalgeirsdóttir, G., Belart, J. M. C., Magnússon, E., Pálsson, F., and Parks, M.: Spatio-Temporal Mass Changes of the Mýrdalsjökull Icecap (Iceland) since2010: Insights from high-Resolution Statistical Modelling., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17705, https://doi.org/10.5194/egusphere-egu25-17705, 2025.

Glaciers are critical to the global socio-ecological system, providing essential ecosystem services and contributing to sea-level rise. They react to ambient atmospheric conditions via surface energy and mass exchanges at the glacier-atmosphere interface and are thus pivotal indicators of ongoing climate change. However, individual atmospheric drivers of glacier change are not well quantified in regional to global glacier modelling, which relies on variants of the temperature-index model due to their ease of use and performance and the reduced need for in-situ observations compared to surface energy balance models.

Leveraging advancements in high-resolution, convection-permitting climate model simulations and a growing body of remotely sensed glacier-specific observations, such as geodetic mass balances and transient snowline altitudes, we explore the possibility of calibrating the surface energy and mass balance model COSIPY using remote observations only as a first step towards applications in unmonitored regions.  

We force COSIPY at Hintereisferner with simulations using the COSMO-CLM model configured with 2.2-km grid spacing from 2000 to 2010 and combine a systematic assessment of the parameter space using Latin Hypercube Sampling and a probabilistic Markov Chain Monte Carlo framework to identify likely posterior parameter values and their associated uncertainties. The calibration outputs are used to assess the energy balance at Hintereisferner and are evaluated against benchmark surface energy balance simulations forced with in-situ observations. We discuss the results in light of commonly used model calibration procedures and validate our results against independent in-situ observations. 

How to cite: Richter, N., Arndt, A., Ban, N., Collier, E., Gampierakis, N., Maussion, F., Umlauf, N., and Nicholson, L.: Leveraging remote observations for calibrating surface energy- and mass balance models: a case study on Hintereisferner, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10390, https://doi.org/10.5194/egusphere-egu25-10390, 2025.

The performance of models for surface mass balance (SMB) builds on reliable atmospheric information as input as well as on in-situ stake measurements for calibration. Both data should best show appropriate quality and spatial coverage. In remote and high mountain regions, in-situ information is often impractical to obtain due to logistical and ressource limitations. Consequently, modellers can often only rely on climate reanalysis data and remotely sensed mass balance observations. As ground-truthing is limited in many mountain regions, this step introduces substantial uncertainties in transient simulations. Given the importance of glaciers as climate indicators and water resources, accurately simulating their evolution is crucial, but cannot be achieved with large uncertainties in forcing and calibration data. This study presents a proof-of-concept to overcome the limitation when estimating glacier-wide mass balance fields by combining mass conservation and stress balance with remotely sensed observations. Target quantity is the 2D SMB field, in particular first-order quantities such as vertical gradients and the equilibrium line altitude (ELA). The flux divergence is calculated using a built-in inversion within the Instructed Glacier Model (IGM).  The model relies on a deep-learning informed surrogate model to simulate ice flow. A sensitivity analysis of this inverse data assimilation was performed to assess the influence of uncertainties of observational input. This analysis emphasises the critical role of ice-thickness measurements. Together with surface velocites, ice thickness controls the spatial pattern and magnitudes in the flux divergence – a key field to infer the unknown SMB. Our approach was further validated in real-world application to Rhône Glacier, Aletsch Glacier and Kanderfirn, demonstrating SMB results largely consistent with available observational records. We extended the application to other glaciers with available SMB measurements and show sound transferability. We are therefore convinced that the resulting SMB fields can be employed to improve the calibration step of melt models of various complexity. As the method exclusively relies on remotely sensed observations it is readily transferible to glacierised regions worldwide. Moreover, the SMB field can provide new insights into poorly constrained precipitation magnitudes over mountainous regions. This is potentially relevant as additional constraints on reanalysis datasets. In summary, this method can seamlessly be integrated into glacier evolution modelling, is readily transferible and adaptable to the specific needs and we are convinced that it will in the future be a valid procedure for melt-model calibration.

How to cite: Zöller, A., Jouvet, G., and Fürst, J.: Ice-Dynamic Constraints on Glacier Climatic Mass Balance using Inverse Technique, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3057, https://doi.org/10.5194/egusphere-egu25-3057, 2025.

Sea level rise projections for the second half of this century exhibit considerable uncertainties, which complicates the implementation of climate change adaptation strategies. These uncertainties stem, in part, from the reliance of ice-flow models on insufficiently constrained parameters such as the englacial temperature and the state of the ice-bed interface. In principle, both parameters can be inferred from radar measurements as the attenuation of the radar signal in the ice is a proxy for the englacial temperature and the strength of the basal reflection depends on the conditions at the basal interface. Here, we focus on developing a new method for inferring attenuation rates from radar measurements for two reasons: (1) existing methods typically provide only depth-averaged attenuation rates and exhibit a strong method dependence of inferred attenuation rates from the same radar dataset, and (2) a better estimate of attenuation rates could additionally improve the interpretation of the basal reflection strength since it relies on attenuation correction. Most contemporary methods infer depth-averaged attenuation rates from the variation of reflection strength of either internal reflectors or the bed reflector with depth. These methods rely on strong assumptions such as comparable reflectivity of internal reflectors or spatially constant reflectivity along the bed reflector. To overcome the dependence on these assumptions, we suggest a different approach that learns the relationship between radar measurements and attenuation rates directly from the data. Due to the lack of radar measurements with known attenuation rates, we simulate realistic radar data with known attenuation rates. We apply Neural Posterior Estimation, a Bayesian machine learning framework, to then infer attenuation rates from radar measurements. Ideally, this approach would not only yield depth-averaged attenuation rates, but also attenuation rate profiles. Here, we present the first results of our work.

How to cite: Muhle, L. S., Moss, G., Schlegel, R., and Drews, R.: Towards a new method for estimating englacial attenuation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-417, https://doi.org/10.5194/egusphere-egu25-417, 2025.

Accurate prediction of ice sheet mass balance requires robust understanding of basal conditions, particularly the ice-bed interface temperature. However, thermal modeling predictions of basal temperature are limited by uncertainties in boundary conditions and sparse in-situ validation data.

Ice-penetrating radar wave attenuation has emerged as a promising large-scale proxy for depth-averaged ice temperature. We present three complementary methods to integrate observed attenuation rates with thermomechanical modeling for improved basal temperature estimation: (1) gradient-assisted MCMC coupled with a fast 1.5D enthalpy model for exact Bayesian inference, (2) Gaussian Process Regression combined with 3D enthalpy model ensembles for exact Bayesian inference, and (3) generative AI integrated with 3D enthalpy model ensembles for approximate Bayesian inference. This multi-method approach offers flexibility in balancing computational demands, inference accuracy, and output continuity.

Application to radar attenuation data from the Amundsen Sea Embayment, West Antarctica, reveals widespread thawed conditions near Pine Island Glacier contrasting with heterogeneous basal conditions upstream of Thwaites Glacier. A pronounced basal temperature gradient between these glaciers suggests a significant flow boundary. This radar-and-model-informed basal temperature field represents a crucial step toward assimilating novel observational constraints and improving sliding mechanics in ice sheet models.

How to cite: Yang, D., Chu, W., and Dawson, E.: Probabilistic Inference of Ice Sheet Basal Temperature with Thermal Modelling and Radar Attenuation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14516, https://doi.org/10.5194/egusphere-egu25-14516, 2025.

Tracing internal reflection horizons (IRHs) in radio-echo sounding data is crucial for understanding ice sheet dynamics and reconstructing past climate conditions. We present an autoregressive generative model designed to trace IRHs iteratively, mimicking the human annotation process. Unlike conventional segmentation-based approaches, which require large training datasets and yield one-shot predictions necessitating extensive post-processing (Moqadam et al. 2024), our model works by estimating a spatial probability map for each annotation mark, conditioned on previously generated marks. This iterative approach emulates human-like tracing by sequentially traversing along each IRH and allows the model to learn from minimal data, resulting in transferability to diverse radar systems.

The model produces interpretable probability maps at each step, providing transparent outputs that human experts can verify directly, without the need for post hoc analyses. Furthermore, avoiding explicit class definitions mitigates the detrimental effects of imbalanced data, which is a common issue in traditional pixel classification methods. The lightweight design of the model – an iterative rather than one-shot approach – improves its suitability for widespread application. This innovative approach presents a significant advancement in automating the annotation of IRHs and provides a robust, interpretable, and adaptable solution for ice sheet radargram analysis.

Hameed Moqadam, Daniel Steinhage, Adalbert Wilhelm, et al. Going deeper with deep learning: automatically tracing internal reflection horizons in ice sheets. ESS Open Archive . October 25, 2024. DOI: 10.22541/essoar.172987463.39597493/v1

How to cite: Moqadam, H., Bojesen, T. A., and Eisen, O.: Autoregressive mark-tracing for radiostratigraphy: A lightweight model for annotating internal reflection horizons in ice sheets, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17824, https://doi.org/10.5194/egusphere-egu25-17824, 2025.

The present-day state of the Antarctic Ice Sheet (AIS) results from millennia of changes in ice accumulation and flow. Understanding ice dynamics over glacial-interglacial cycles is crucial for accurately representing the current state of the AIS in models and making reliable projections. This study leverages the growing pool of traced and dated internal layers (isochrones) to characterise regions around deep ice core sites in Antarctica, focusing on ice divides in which ice flows on hundreds of thousands of years timescale. We employ a thermomechanically-coupled 3D ice sheet model (PISM) to simulate ice flow over glacial-interglacial cycles in these regions. First, we implement direct reconstructions of surface temperature and accumulation from deep ice cores, bypassing conventional climate index approaches in improving the thermal state and constraining the isochronal structure in the upper part of the ice. Second, we improve the ice rheology and constrain the model parameter space by minimising the mismatch between observed and modelled isochrone elevations closer to bedrock. Finally, we reduce uncertainties in basal thermal conditions through direct comparison with measured borehole profiles and further spatial calibration of isochronal geometries.
This methodology emphasises the importance of reliable boundary conditions in ice sheet models for accurately representing past ice dynamics. Our work seeks to deepen our understanding of AIS dynamics on glacial-interglacial timescales and provide improved paleo-informed initialisations for AIS projections.

How to cite: Hermant, A., Višnjević, V., Bodart, J., Wirths, C., and Sutter, J.: Constraining glacial-interglacial Antarctic Ice Sheet dynamics using ice core and isochronal records, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9843, https://doi.org/10.5194/egusphere-egu25-9843, 2025.

Constraining paleo-modelling results of the West Antarctic Ice Sheet (WAIS) has primarily been restricted to using individual point-based measurements such as ice and sediment cores. However, these records only provide a one-dimensional picture of temperature/accumulation and ice-sheet/grounding-line extent respectively. Additionally, the extent to which these measurements are representative of the wider region in which they are situated is uncertain. This in turn impacts our ability to constrain paleo simulations of the ice sheet from physics-based models. Here, we make use of a spatially extensive age-depth model, compiled over much of the Pine Island and Thwaites glacier catchments from radar-detected isochrones, to constrain paleo simulations from the three-dimensional ice-sheet model PISM. We present initial results and assess the mismatches that exist between the observed dated isochrones from the radar and the modelled isochrones obtained from our simulations, focusing primarily on the Last Glacial Maximum and Holocene period, a time during which the ice-sheet most likely transitioned into today's intergacial state in a non-linear fashion. We aim to refine the ice-sheet model’s parameters based on this mismatch analysis in isochrone elevations, thus providing us with a spatially constrained evolution of the two glacier catchments that go beyond the typical one-dimensional constraints used so far over the WAIS.

How to cite: Bodart, J., Višnjević, V., Hermant, A., Wirths, C., Spezia, E., and Sutter, J.: Modelling the Evolution of West Antarctica Through the Last Glacial Maximum and Holocene Constrained by Radar Isochrones, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3248, https://doi.org/10.5194/egusphere-egu25-3248, 2025.

Improving our understanding of past ice dynamics is essential for robust simulations of the future evolution of Antarctic Ice sheet and consequent sea-level rise projections. A major challenge in reconstructing paleo ice flow is the limited availability of temporal and spatial proxies to constrain ice evolution. Reconstructions on continental and regional scales often rely on broad ensembles constrained by present-day observations or sparse point data, such as past grounding line positions at specific locations. This sparse temporal and spatial coverage often proves inadequate for reconstructing the past conditions of the Antarctic Ice Sheet.

In this study, we employ Antarctica’s radar obtain stratigraphy, a repository of past changes in ice dynamics, climate and basal conditions, to constrain spatial and temporal changes in the evolution of the Dronning Maud Land, East Antarctica across the last 200kyrs. We use PISM to model ice flow, exploring the ice dynamics parameter space, and the influence of different geothermal fluxes, RACMO versions, grid sizes and basal parametrizations. To simulate the temporal climate signal, we use the climate index approach as well as accumulation information from EDML ice core. Finally, isochrones allow us to test and compare climate reconstructions and ice flow parameterizations, identify when mismatches occur during simulations, and distinguish between the effects of surface and basal processes.

How to cite: Višnjević, V., Bodart, J., Hermant, A., Spezia, E., Wirths, C., and Sutter, J.: Isochronal insights into ice flow evolution during the Last Glacial Period in Dronning Maud Land, Antarctica , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9847, https://doi.org/10.5194/egusphere-egu25-9847, 2025.

The forward motion of glaciers and ice sheets results from two components: internal deformation and basal sliding, with the latter accounting almost entirely for the high speeds attained by ice streams. Even with direct access to the ice-bed interface (e.g., through a borehole), basal motion cannot be measured directly and must be derived through modelling. In this work, we compare three previously developed mathematical frameworks for deriving englacial and basal sliding velocities from borehole tilt observations. These methods address different tensional configurations: 1) pure plane strain, 2) plane strain with an ad-hoc extension component optimized for scenarios with a limited number of tiltmeters, and 3) plane strain with a combined extension-compression component, restricted to regions with negligible lateral drag. The velocity is reconstructed by measured variations in tilt angle along boreholes drilled to the bed. For synthetic tilt curves that are representative of a variety of tensional states, and for each of the modelling frameworks above, we assess the limitations and propagation of errors in the reconstructed velocity profiles and basal velocities. We further discuss the optimal number and location of borehole tiltmeters that minimize errors in the estimated sliding velocity. This work offers practical guidance on an upcoming borehole campaign at the Grenzgletscher, Switzerland, aimed at characterizing the onset of basal sliding at frozen/temperate basal transitions.

How to cite: Gerli, C. and mantelli, E.: Comparing methods for estimating basal velocity and internal deformation at the Grenzgletscher, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16121, https://doi.org/10.5194/egusphere-egu25-16121, 2025.

Realistic simulations of large ice sheets under various climate conditions are crucial to estimate future ice sheet (in)stability and melt. Here, we focus on the Greenland ice sheet and its evolution under the climate of the last glacial cycle using the ice sheet model Yelmo. Our previous work demonstrated the value of applying the layer tracer model Englacial Layer Simulation Architecture (ELSA) together with dated radiostratigraphy data to validate and calibrate the Yelmo-modeled ice sheet. 

However, an outstanding complication is that the thickness of modeled isochrones not only depends on dynamical thinning but also on the original thickness upon deposition (surface mass balance). While our earlier simulations used a simplified representation of past climates and employed a positive degree day scheme to estimate the surface mass balance, we now attempt to improve the representation of the boundary conditions by using the more detailed Bergen Snow Simulator (BESSI). BESSI simulates all surface and internal fluxes of heat and mass explicitly and outputs surface mass balance while still being computationally efficient. 

With BESSI providing a more realistic layer deposition thickness, ELSA tracing the modeled isochronal layers, and radiostratigraphy providing reconstructed isochrones as a comparison quantity, we have a comprehensive framework to evaluate the climate input and ice dynamics of our simulations and can work towards a realistic modeled representation of the Greenland ice sheet over the last glacial cycle.

How to cite: Rieckh, T. and Born, A.: A multi-model approach for more realistic simulations of the Greenland ice sheet during the last glacial cycle, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6268, https://doi.org/10.5194/egusphere-egu25-6268, 2025.