Preprints
https://doi.org/10.5194/egusphere-2025-3401
https://doi.org/10.5194/egusphere-2025-3401
11 Aug 2025
 | 11 Aug 2025
Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

The Open Global Glacier Data Assimilation Framework (AGILE) v0.1

Patrick Schmitt, Fabien Maussion, Daniel N. Goldberg, and Philipp Gregor

Abstract. 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 (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 by the diffusive nature of glacier dynamics, 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.

Competing interests: At least one of the (co-)authors is a member of the editorial board of Geoscientific Model Development.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
Share
Patrick Schmitt, Fabien Maussion, Daniel N. Goldberg, and Philipp Gregor

Status: open (until 06 Oct 2025)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Patrick Schmitt, Fabien Maussion, Daniel N. Goldberg, and Philipp Gregor
Patrick Schmitt, Fabien Maussion, Daniel N. Goldberg, and Philipp Gregor

Viewed

Total article views: 157 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
144 10 3 157 10 4 5
  • HTML: 144
  • PDF: 10
  • XML: 3
  • Total: 157
  • Supplement: 10
  • BibTeX: 4
  • EndNote: 5
Views and downloads (calculated since 11 Aug 2025)
Cumulative views and downloads (calculated since 11 Aug 2025)

Viewed (geographical distribution)

Total article views: 155 (including HTML, PDF, and XML) Thereof 155 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 30 Aug 2025
Download
Short summary
To improve large-scale understanding of glaciers, we developed a new data assimilation method that integrates available observations in a dynamically consistent way, while taking their timestamps into account. It is designed to flexibly include new glacier data as it becomes available. We tested the method with idealized experiments and found promising results in terms of accuracy and efficiency, showing strong potential for real-world applications.
Share