Samuel Urs Nussbaumer

Current glacier model-data comparison methods often lack quantitative, automated validation for reconstructing complex palaeoglaciers and palaeo ice fields over millennia. Rigorous validation and automatic evaluation is critical for improving the reliability of glacier reconstructions and their use in understanding past climate and future projections. We introduce an open-source, Python-based comparison tool (GLAMODAT version 1.0) that automatically evaluates palaeoglacier model simulations against dated constraints on glacier history. GLAMODAT successfully quantifies model-data mismatches and demonstrates robustness in both synthetic and real-world case studies. It effectively tracks multiple phases of advance and retreat periods within mountain valleys. It is designed for validating single palaeoglacier simulations or entire ensembles by integrating weighted misfit scoring and adaptive parameter space exploration. It enables advancements in palaeoglacier reconstructions, climate reconstructions, and parameter studies in glacier modelling.

The AlpIce‐1 database is the first compilation of published geomorphological and geochronological markers constraining build‐up, culmination and decay of glaciers from the Last Glacial Maximum through the Holocene across the entire European Alps. AlpIce‐1 integrates more than 4200 surface‐exposure and radiocarbon ages, homogenized, quality controlled and evaluated for their palaeoglaciological context, compiled from 268 peer‐reviewed publications and over 1300 sites across the European Alps. The database includes ages originally obtained from a wide range of landforms and deposits including moraine ridges, lakes, mires, sediment outcrops, ice cores, bedrock surfaces, caves and archaeological sites. It documents chronological constraints on more than 1400 palaeoglacial events, including past ice‐margin stabilization, ice (re‐)advance, ice retreat, ice‐free or ice‐cover conditions. A GIS catalogue of more than 260 dated landforms (e.g. moraines, rock glaciers, landslide deposits) complements the database. AlpIce‐1 is designed as an open‐access resource supporting both field and modelling studies across the Alps and is intended for all communities interested in the late Quaternary. In light of emerging next‐generation numerical ice models, AlpIce‐1 offers an extensive and robust empirical basis for evaluating future (palaeo)glacier simulations.

Reconstructing the transport histories and provenances of glacial sediments and ice-contact deposits (e.g. tills, moraines) in formerly glaciated regions remains a major challenge, particularly at icefield- to ice-sheet scales and over multi-millennial timescales. Yet such reconstructions are central to key questions in Quaternary science, including estimates of past glacial erosion rates and sediment fluxes, the role of subglacial sediment storage in erosion reduction, or the reconstruction of past ice-flow dynamics, ice divides, and transfluences. While numerical modelling can enable one to reproduce past glacial sediment transport via coupling glacier models with particle tracking, this becomes computationally unfeasible over large spatial domains and paleo timescales using traditional computing. As a result, no study to date has simulated glacial sediment transport using large particle numbers (tens of millions) across continental-scale icefields such as the one occupying the European Alps during the Last Glacial Maximum (LGM). Here, we overcome this limitation using the Instructed Glacier Model (IGM), which allows the coupling of 3D Lagrangian particle tracking with high-resolution glacier simulations, both accelerated on Graphics Processing Units (GPU). This unlocks the modelling of ice advection of millions of particles at minimal additional computational cost, allowing simulations of glacial sediment transport across the European Alps over multi-millennial timescales (40–18 ka) and at the unprecedented spatial resolution of 300 m. We achieve ∼ 50× faster computation tracking 20 million particles across the Alps using a single GPU instead of 60 CPU threads. In doing so, we produce the first Alps-wide modelling reconstruction of glacial sediment transport during the LGM, using process-based particle seeding schemes to represent both subglacial (e.g. abrasion, plucking) and supraglacial (e.g. rockfall, landslides) sediment sourcing. Results are analysed through complementary “sink-to-source” (deposit provenance) and “source-to-sink” (potential depositional pathways) analyses, enabling us to reconstruct the LGM glacial transport of numerous ice-contact deposits and surface lithologies across the Alps. We find that supraglacially sourced glacial sediments are typically eroded earlier, experience longer glacier residence times, and undergo greater cumulative ice-free exposure than those of subglacial origin, with implications for the interpretation of cosmogenic nuclide inheritance in glacial deposits. Our new coupled glacier-particle modelling framework opens avenues for quantitative model-data comparisons using glacial geomorphology and provides a powerful tool for reconstructing paleo ice dynamics, sediment provenance, and Quaternary glacial landscape evolution.

Reconstructing the transport histories and provenances of glacial sediments and ice-contact deposits (e.g. tills, moraines) in formerly glaciated regions remains a major challenge, particularly at icefield- to ice-sheet scales and over multi-millennial timescales. Yet such reconstructions are central to key questions in Quaternary science, including estimates of past glacial erosion rates and sediment fluxes, the role of subglacial sediment storage in erosion buffering, or the reconstruction of past ice-flow dynamics, ice divides, and transfluences. While numerical modelling can enable one to reproduce past glacial sediment transport via coupling glacier models with Lagrangian particle tracking, this becomes computationally unfeasible over large spatial domains and paleo timescales using traditional computing. As a result, no study to date has simulated glacial sediment transport using large particle numbers (tens of millions) across continental-scale icefields such as the European Alps during the Last Glacial Maximum (LGM): a pre-requisite given the ubiquitous nature of sediments in glacier systems. In this study, we overcome this limitation through a new coupling of 3D Lagrangian particle tracking with Graphics-Processing-Units (GPU)-accelerated, high-resolution glacier simulations based on the deep-learning-enhanced Instructed Glacier Model (IGM). Our approach unlocks the ice advection of tens of millions of particles at minimal additional computational cost, allowing simulations of glacial sediment transport across the European Alps over multi-millennial timescales (40–18 ka) and at an unprecedented spatial resolution of 300 m. In doing so, we produce the first Alps-wide modelling reconstruction of glacial sediment transport during the LGM, using process-based particle seeding schemes to represent both subglacial (e.g. abrasion, plucking) and supraglacial (e.g. rockfall, landslides) sediment sourcing. Results are analysed through complementary ‘sink-to-source’ (deposit provenance) and ‘source-to-sink’ (potential depositional pathways) analyses, enabling us to reconstruct the LGM glacial transport of numerous ice-contact deposits and surface lithologies across the Alps. We find that supraglacially sourced glacial sediments are typically eroded earlier, experience longer glacier residence times, and undergo greater cumulative ice-free exposure than those of subglacial origin, with implications for the interpretation of cosmogenic nuclide inheritance in glacial deposits. Our new coupled glacier-particle modelling framework opens avenues for quantitative model-data comparisons using glacial geomorphology and provides a powerful tool for reconstructing paleo ice dynamics, sediment provenance, and Quaternary glacial landscape evolution.

Reconstructing the transport histories and provenances of glacial sediments and ice-contact deposits (e.g. tills, moraines) in formerly glaciated regions remains a major challenge, particularly at icefield- to ice-sheet scales and over multi-millennial timescales. Yet such reconstructions are central to key questions in Quaternary science, including estimates of past glacial erosion rates and sediment fluxes, the role of subglacial sediment storage in erosion buffering, or the reconstruction of past ice-flow dynamics, ice divides, and transfluences. While numerical modelling can enable one to reproduce past glacial sediment transport via coupling glacier models with Lagrangian particle tracking, this becomes computationally unfeasible over large spatial domains and paleo timescales using traditional computing. As a result, no study to date has simulated glacial sediment transport using large particle numbers (tens of millions) across continental-scale icefields such as the European Alps during the Last Glacial Maximum (LGM): a pre-requisite given the ubiquitous nature of sediments in glacier systems. In this study, we overcome this limitation through a new coupling of 3D Lagrangian particle tracking with Graphics-Processing-Units (GPU)-accelerated, high-resolution glacier simulations based on the deep-learning-enhanced Instructed Glacier Model (IGM). Our approach unlocks the ice advection of tens of millions of particles at minimal additional computational cost, allowing simulations of glacial sediment transport across the European Alps over multi-millennial timescales (40–18 ka) and at an unprecedented spatial resolution of 300 m. In doing so, we produce the first Alps-wide modelling reconstruction of glacial sediment transport during the LGM, using process-based particle seeding schemes to represent both subglacial (e.g. abrasion, plucking) and supraglacial (e.g. rockfall, landslides) sediment sourcing. Results are analysed through complementary ‘sink-to-source’ (deposit provenance) and ‘source-to-sink’ (potential depositional pathways) analyses, enabling us to reconstruct the LGM glacial transport of numerous ice-contact deposits and surface lithologies across the Alps. We find that supraglacially sourced glacial sediments are typically eroded earlier, experience longer glacier residence times, and undergo greater cumulative ice-free exposure than those of subglacial origin, with implications for the interpretation of cosmogenic nuclide inheritance in glacial deposits. Our new coupled glacier-particle modelling framework opens avenues for quantitative model-data comparisons using glacial geomorphology and provides a powerful tool for reconstructing paleo ice dynamics, sediment provenance, and Quaternary glacial landscape evolution.

Mountain glaciers are critical elements of the Earth’s hydrological and climate systems. The rapid changes in glaciers due to climate change hinder our ability to monitor and address associated risks effectively. To address these challenges, we present the Digital Twin Component for Glaciers (DTC Glaciers), part of ESA’s Digital Twin Earth (DTE) programme. In this presentation, we will demonstrate the early prototype of the DTC Glaciers system, developed through close co-design with our stakeholders in the hydropower and water sectors. The demonstration will highlight current capabilities, including regional glacier mass-balance assessment, runoff estimation, and user-informed scenarios. We will also share key lessons learned from Phase 1, focussing on data assimilation of heterogeneous datasets, the practicalities of building adaptive architectures, and the challenges of meeting diverse user needs within a unified framework. Despite these challenges, DTC Glaciers offers a well defined test case to assess the transformative potential of digital twins in climate risk assessments.

Measuring glacier mass change worldwide is essential to document the impact of climate change, understand glacier-related hazards and assess the contribution of the cryosphere to sea-level rise. Over the last two decades, three remote-sensing techniques have been used to measure glacier mass change: digital elevation model (DEM) differencing, altimetry and gravimetry. The Glacier Mass Balance Intercomparison Exercise (GlaMBIE, ESA-funded) aims to compile, homogenize and combine these regional glacier mass change observations. One intriguing outcome from the first GlaMBIE phase (2022–24) was a systematic difference of region-wide elevation change measured using ASTER DEM differencing (dDEM) and CryoSat-2 radar altimetry. In most cases, dDEM resulted in more negative values in all glacier regions with an average regional difference of 0.08 ± 0.07 m w.e. yr-1 (The GlaMBIE Team, 2025). The work presented here is part of the second GlaMBIE phase (2025–27). It aims at describing in more detail and understanding the differences between these two techniques. We focus on major Icelandic ice caps as test sites as they present a good coverage for both techniques and are also covered with high resolution, precise dDEM data (e.g., airborne lidar in 2013 and Pléiades in 2020 for Hofsjökull ice cap). Our preliminary results indicate that the differences between published ASTER (Hugonnet et al., 2021) and CryoSat-2 (Jakob and Gourmelen, 2023) estimates are also found at ice cap and glacier scale. Next, we plan to compare the methods over the exact same period, and evaluate them using accurate validation data. We will also test the sensitivity to the software used to generate the DEMs, to alternative processing of CryoSat-2 data, and to the post-processing of the data (altimetry and DEMs) to find the key reasons for the systematic difference.

Reconstructing the transport histories and provenances of glacial sediments and ice-contact deposits (e.g. tills, moraines) in formerly glaciated regions remains a major challenge, particularly at icefield- to ice-sheet scales and over multi-millennial timescales. Yet such reconstructions are central to key questions in Quaternary science, including estimates of past glacial erosion rates and sediment fluxes, the role of subglacial sediment storage in erosion buffering, or the reconstruction of past ice-flow dynamics, ice divides, and transfluences. While numerical modelling can enable one to reproduce past glacial sediment transport via coupling glacier models with Lagrangian particle tracking, this becomes computationally unfeasible over large spatial domains and paleo timescales using traditional computing. As a result, no study to date has simulated glacial sediment transport using large particle numbers (tens of millions) across continental-scale icefields such as the European Alps during the Last Glacial Maximum (LGM): a pre-requisite given the ubiquitous nature of sediments in glacier systems. In this study, we overcome this limitation through a new coupling of 3D Lagrangian particle tracking with Graphics-Processing-Units (GPU)-accelerated, high-resolution glacier simulations based on the deep-learning-enhanced Instructed Glacier Model (IGM). Our approach unlocks the ice advection of tens of millions of particles at minimal additional computational cost, allowing simulations of glacial sediment transport across the European Alps over multi-millennial timescales (40–18 ka) and at an unprecedented spatial resolution of 300 m. In doing so, we produce the first Alps-wide modelling reconstruction of glacial sediment transport during the LGM, using process-based particle seeding schemes to represent both subglacial (e.g. abrasion, plucking) and supraglacial (e.g. rockfall, landslides) sediment sourcing. Results are analysed through complementary ‘sink-to-source’ (deposit provenance) and ‘source-to-sink’ (potential depositional pathways) analyses, enabling us to reconstruct the LGM glacial transport of numerous ice-contact deposits and surface lithologies across the Alps. We find that supraglacially sourced glacial sediments are typically eroded earlier, experience longer glacier residence times, and undergo greater cumulative ice-free exposure than those of subglacial origin, with implications for the interpretation of cosmogenic nuclide inheritance in glacial deposits. Our new coupled glacier-particle modelling framework opens avenues for quantitative model-data comparisons using glacial geomorphology and provides a powerful tool for reconstructing paleo ice dynamics, sediment provenance, and Quaternary glacial landscape evolution.

Glaciers are crucial indicators of climate change, and reconstructing their past geometries helps to understand past climate fluctuations. Various methods exist for reconstructing past glaciers, including simple power-law scaling and advanced GIS-based techniques that incorporate glacier outlines or surface hypsometry. However, these methods have limitations, such as not explicitly accounting for the physics of ice flow or mass conservation. Numerical glacier models, such as the Instructed Glacier Model (IGM), can overcome these limitations by incorporating ice-flow dynamics and mass conservation. This study presents the first Alps-wide, three-dimensional, model-derived reconstruction of glacier surfaces during the Little Ice Age in the European Alps, a period crucial for understanding pre-industrial natural climate variability. We simulate glaciers to match the empirically mapped Little Ice Age maximum extent at a resolution of 50 m. The simulation of the geometry of all glaciers of the European Alps resulted in a total ice volume of 283±42 km3. The reconstruction reveals regional and local patterns of equilibrium line altitudes derived separately for each glacier. These spatial patterns are influenced by factors such as air temperature, precipitation and shortwave radiation, highlighting the complex interplay of climatic and topographic factors in reconstructing these glaciers and their mass fluxes. A sensitivity analysis indicates an uncertainty of up to 14 % in the total ice volume and minimal sensitivity to parameter modifications for the equilibrium line altitude. Future work could include more sophisticated surface mass balance implementations to better understand the equilibrium line altitude patterns.