Patrick Schmitt

Modeling frontal ablation of marine-terminating glaciers is critical for accurately modeling mass change but remains challenging. We present a hybrid, physically consistent and numerically efficient modelling framework that integrates the Simple Estimator of Retreat Magnitude and ice flux (SERMeQ) into two coupled global glacier models (OGGM and PyGEM). The adaptive particle‑batch smoother, a Bayesian data assimilation method, is used for joint calibration of all parameters. The coupled framework simulates monthly length changes along flowlines and mass‑balance components of individual glaciers. Applied to 71 marine-terminating glaciers across Svalbard during the period of 2000–2019, the model simulates distinct seasonal variations and annual length changes of glacier calving fronts, while reproducing decadal climatic mass balance estimates well. However, frontal ablation tends to be overestimated possibly due to uncertainties in initial geometry and modelled ice velocities. Overall, the good alignment of the regional distributions highlights that the modeling framework, including the efficient joint Bayesian calibration, provides a promising tool for large-scale frontal ablation modeling in glacier evolution models.

Modeling frontal ablation of marine-terminating glaciers is critical for accurately modeling mass change but remains challenging. We present a hybrid, physically consistent and numerically efficient modelling framework that integrates the Simple Estimator of Retreat Magnitude and ice flux (SERMeQ) into two coupled global glacier models (OGGM and PyGEM). The adaptive particle‑batch smoother, a Bayesian data assimilation method, is used for joint calibration of all parameters. The coupled framework simulates monthly length changes along flowlines and mass‑balance components of individual glaciers. Applied to 71 marine-terminating glaciers across Svalbard during the period of 2000–2019, the model simulates distinct seasonal variations and annual length changes of glacier calving fronts, while reproducing decadal climatic mass balance estimates well. However, frontal ablation tends to be overestimated possibly due to uncertainties in initial geometry and modelled ice velocities. Overall, the good alignment of the regional distributions highlights that the modeling framework, including the efficient joint Bayesian calibration, provides a promising tool for large-scale frontal ablation modeling in glacier evolution models.

Modeling frontal ablation of marine-terminating glaciers is critical for accurately modeling mass change but remains challenging. We present a hybrid, physically consistent and numerically efficient modelling framework that integrates the Simple Estimator of Retreat Magnitude and ice flux (SERMeQ) into two coupled global glacier models (OGGM and PyGEM). The adaptive particle‑batch smoother, a Bayesian data assimilation method, is used for joint calibration of all parameters. The coupled framework simulates monthly length changes along flowlines and mass‑balance components of individual glaciers. Applied to 71 marine-terminating glaciers across Svalbard during the period of 2000–2019, the model simulates distinct seasonal variations and annual length changes of glacier calving fronts, while reproducing decadal climatic mass balance estimates well. However, frontal ablation tends to be overestimated possibly due to uncertainties in initial geometry and modelled ice velocities. Overall, the good alignment of the regional distributions highlights that the modeling framework, including the efficient joint Bayesian calibration, provides a promising tool for large-scale frontal ablation modeling in glacier evolution models.

Modeling frontal ablation of marine-terminating glaciers is critical for accurately modeling mass change but remains challenging. We present a hybrid, physically consistent and numerically efficient modelling framework that integrates the Simple Estimator of Retreat Magnitude and ice flux (SERMeQ) into two coupled global glacier models (OGGM and PyGEM). The adaptive particle‑batch smoother, a Bayesian data assimilation method, is used for joint calibration of all parameters. The coupled framework simulates monthly length changes along flowlines and mass‑balance components of individual glaciers. Applied to 71 marine-terminating glaciers across Svalbard during the period of 2000–2019, the model simulates distinct seasonal variations and annual length changes of glacier calving fronts, while reproducing decadal climatic mass balance estimates well. However, frontal ablation tends to be overestimated possibly due to uncertainties in initial geometry and modelled ice velocities. Overall, the good alignment of the regional distributions highlights that the modeling framework, including the efficient joint Bayesian calibration, provides a promising tool for large-scale frontal ablation modeling in glacier evolution models.

Accurate estimation of frontal ablation of water‑terminating glaciers is essential for assessing global glacier mass change and projecting sea‑level rise. We present a hybrid framework that couples a SERMeQ‑based frontal‑ablation component with climatic mass‑balance from PyGEM and ice dynamics from OGGM, and we introduce an adaptive particle‑batch smoother to jointly calibrate all model parameters simultaneously. The model simulates centreline length change and mass‑balance components at monthly resolution and updates flow‑line geometry accordingly. Calibration assimilates both decadal averaged geodetic mass‑balance estimates and remote‑sensing annual timeseries terminus‑position changes, constraining the coupled dynamics and ablation processes within a single, physically consistent framework. Applied regionally to 71 tidewater glaciers in Svalbard, the framework reproduces observed seasonal behaviour and hindcasts, while providing improved projections of future glacier evolution. These results offer more robust regional estimates of contributions to sea‑level rise and freshwater availability and identify priorities for further reducing uncertainties in frontal‑ablation estimates.

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.

Recognising the global importance and vulnerability of mountain glaciers, the United Nations General Assembly declared 2025 the International Year of Glaciers’ Preservation, highlighting the critical role of glaciers in the hydrological cycle and the growing societal risks associated with their rapid decline. These concerns are well founded: glaciers worldwide are retreating rapidly, yet projections have traditionally focused on changes in mass and area rather than on the fate of individual glaciers.Here, we quantify the future evolution of the global number of glaciers and introduce the concept of peak glacier extinction: the year in which the largest number of individual glaciers is projected to disappear. Using three global glacier models and the Randolph Glacier Inventory v6.0, we project the fate of more than 200,000 glaciers worldwide under four policy-relevant global warming scenarios by 2100 (+1.5 °C, +2.0 °C, +2.7 °C and +4.0 °C relative to pre-industrial levels). A glacier is classified as extinct when its area falls below 0.01 km² or its remaining volume declines to less than 1% of its initial value.Across all scenarios, we identify a pronounced mid-century peak in glacier extinction. Under a +1.5 °C pathway, this peak occurs around 2041, with approximately 2,000 glaciers disappearing per year, whereas under a +4.0 °C scenario it shifts to the mid-2050s and intensifies to nearly 4,000 glacier extinctions annually. Regional differences reflect contrasts in glacier size distributions and climatic conditions. Our results highlight the urgency of ambitious climate action. Whether the world faces the loss of 2,000 or 4,000 glaciers per year by mid-century, and whether roughly 20,000 or 100,000 glaciers remain by the end of the century, will be determined by near-term policy and societal choices made today.

The growing availability of glacier observations poses a challenge for models to integrate this heterogeneous information in a dynamically consistent way. At the same time, estimates of current glacier volume and area remain uncertain, as many global inventories and thickness datasets date back to the early 2000s. We present the Open Global Glacier Data Assimilation Framework, named AGILE, a time-dependent variational method inspired by 4D-Var data assimilation. AGILE is built on a reimplementation of the OGGM flowline glacier evolution model in PyTorch, enabling full differentiability through automatic differentiation (AD). We test AGILE v0.1 in a series of idealized experiments designed to reflect common initialization and calibration scenarios in global glacier modeling. The goal is to recover glacier bed topography and distributed ice volume in 2020 through transient calibration, based on dynamical simulations starting in 1980. In these experiments, we assume a perfectly known mass balance and fixed ice dynamics parameters. While this setup simplifies real-world complexity, it allows us to isolate and evaluate the core functionality of the approach. Our results show that AGILE efficiently optimizes multiple control variables by leveraging AD-derived gradients, requiring only a few iterations to substantially improve upon initial guesses. We also examine the potential to reconstruct earlier glacier states (e.g., in 1980) without direct observations and find that this is fundamentally limited because glacier dynamics are governed by a diffusion equation, which leads to a loss of information about past states over time, even in an idealized setting. Overall, our experiments demonstrate AGILE’s potential as a flexible and efficient data assimilation framework. Its ability to integrate diverse datasets in a dynamically consistent manner makes it a promising tool for future real-world glacier modeling applications.