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Bring us the RGI ID of your favorite mountain glacier. Anywhere. In one minute we will simulate the evolution of your glacier with the Instructed Glacier Model (IGM, https://github.com/jouvetg/igm). IGM is a user-friendly python code that can model 3D glacier evolution with initial data assimilation based on (deep-learned) high-order ice mechanics. The use of deep-learning makes the overall model computationally highly efficient, especially on Graphics Processing Units (GPUs). The workflow is the following: first we use the Open Global Glacier Model (OGGM, https://oggm.org/) to download and prepare all observation data (surface topography, ice mask, surface ice speeds, …) in a consistent way. Second, we perform data assimilation with IGM to seek for the ice thickness and ice-flow parameters that best match observations. Last, we define an ELA-based climate forcing and simulate the evolution of the glacier over the coming centuries with IGM. One more minute? In that case, we will activate the particle-tracking scheme, and you can watch the motion of the particles advected within the 3D ice flow of your glacier to visualise the velocity field., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Recent advances in global geodetic mass balance and velocity assessments open new possibilities for global glacier model calibration and initialization. As the amount of data available for calibration increases in volume and complexity, how to best combine all these heterogeneous observations to initialize a dynamically consistent glacier evolution model?In this contribution, we present the Open Global Glacier Data Assimilation Framework (AGILE), which iteratively adapts control variables to minimize a cost function penalizing mismatch to observations (i.e. an 'inversion' of the observations). AGILE uses automatic differentiation (AD) and the machine learning framework PyTorch to obtain the control variable sensitivities for efficient minimization. The flexible nature of AD allows the combination of temporally and spatially heterogeneous observational sources with a variety of control variables (e.g. glacier bed heights, mass-balance parameters, initial ice thickness, ...). Moreover, it makes it possible to switch parts of the modelling chain with differentiable counterparts.We will demonstrate the capabilities of AGILE with idealized experiments using a re-implementation of an existing numerical model of glacier evolution (the Open Global Glacier Model, OGGM). We define bed height as well as an initial ice thickness distribution in the past as control variables, andconstrain our inversion with synthetic observations orientated towards globally available data sets. Our approach removes the need for equilibrium assumptions or an 'apparent mass balance' specification, both required in other model-based bed-height estimates. Finally, we will discuss the added value of AGILE and upcoming challenges for a global operational application., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

The Randolph Glacier Inventory (RGI) is a globally complete collection of digital glacier outlines that plays a crucial role in global and regional glaciological research. We introduce the RGI version 7.0, which is our best estimate of global glacier outlines (excluding the two ice sheets) around the year 2000. Unlike previous versions which were compiled manually, RGI7 is generated from the Global Land Ice Measurements from Space (GLIMS) glacier database, ensuring full traceability of single outlines to their original authors. The dataset is generated with Python scripts selecting outlines according to community decisions during an open review process. About 73% of the outlines (42% of the total area) in RGI7 are obtained from new inventories. This led to considerable quality improvements especially in High Mountain Asia, parts of Alaska, northern Canada, northern Greenland, Caucasus and Middle East, the Tropics, South America, New Zealand, and the Antarctic Periphery. Strikingly, the number of outlines increased by 27%, from 215,547 to 274,589. Smaller glaciers are now better inventoried and snow fields are less likely to be mapped as glaciers. Since these two improvements compensated each other, the total area in RGI7 (706,735 km2) is only 0.1% more than in RGI6. Regionally, area changes can be considerably larger (e.g. -18% in Low Latitudes, +9% in South East Asia). The new RGI generation process is open-source, fully reproducible and easily adaptable, making future updates straightforward to generate., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Projections of glacier mass change impact sea level rise, water resources, natural hazards, tourism, and cultures. Here we present projections of global glacier changes, excluding the ice sheets, for ensembles of Shared Socioeconomic Pathways that are aggregated into policy-relevant temperature change scenarios. These temperature change scenarios highlight the impact that reducing temperature increase has on reducing glacier mass loss, glacier contributions to sea-level rise, and preventing widespread deglaciation in many regions. Projections are performed using a hybrid of the Python Glacier Evolution Model (PyGEM) and Open Global Glacier Model (OGGM), which are two open-source large-scale glacier evolution models. Major model improvements will be discussed including advances to PyGEM’s Bayesian calibration scheme, modeling of physical processes (e.g., glacierdynamics, frontal ablation, and debris cover), and the integration of new glacier change datasets. We will conclude by discussing PyGEM’s future direction, the incorporation of upcoming glacier change datasets, and continued improvements to support its use by the greater community thereby reducing the barrier to conduct research on global glacier evolution modeling., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

The region of High Mountain Asia hosts the largest assemblage of glaciers outside the poles and is already subject to regional water allocation conflicts and geomorphic hazards due to increasing glacier mass loss and global warming. Despite the need for improved glacier health monitoring, in situ measurements of glaciers are sparse and focused on a few selected glaciers.This imposes limitations for the applicability of surface energy balance (SEB) models as their usage is limited to glaciers with sufficient observations to address the dependency on reliant forcing data and the need for calibrated model parameters, often extracted using previous empirical knowledge or tuned with observations. Therefore, most regional glacier mass change studies are obtained via remote-sensing based geodetic estimates or variations of the temperature-index model that do not allow for the quantification and attribution of the driving forces of glacier melt.In this study, we aim to evaluate the possibility of extending SEB simulations from local to regional scale by introducing a semi-automated model parameter calibration of the open-source COSIPY model forced with high resolution climate simulations for the period of 2010 to 2020.We do so by employing a cost-function informed Markov Chain Monte Carlo sampling algorithm which evaluates the mismatch between existing snowline and geodetic mass balance observations.The calibration results will be used to investigate the atmospheric drivers of few selected glaciers and compared to existing studies and measurements to evaluate both the feasibility of the applied framework and the uncertainty due to parameter choices.&nbsp, The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Concurrent with atmospheric warming, glaciers around the world are rapidly retreating affecting global sea level and streamflow. Projections show considerable mass losses over the 21st century, however, mass losses vary strongly between regions and emission scenarios. In some regions with relatively little ice cover projections driven by high emission scenarios show near-complete deglaciation by the end of this century, while in polar regions relative mass losses are typically in the order of a few tenths of percent relative to the present. The mass losses modify local runoff regimes and lead to increases in glacier runoff in some regions but to decreases in others. Projected global glacier mass losses by the end of the 21st century correlate linearly with global mean temperature increase indicating that reducing global warming will limit future mass losses and their impacts., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)