Working Group on Continuity approaches for mass balance Intercomparison eXercise (ContinuIX) (2025-2029)

WG proposal

WG co-chairs

  • Marin Kneib, ETH Zürich & WSL, Switzerland
  • Evan Miles, University of Zürich, Switzerland
  • Victor Devaux-Chupin, University of Alaska, USA
  • Johannes Fürst, Friedrich-Alexander-Universität, Germany
  • Maaike Izeboud, Vrije Universiteit Brussel, Belgium
  • Albin Wells, Carnegie Mellon University, USA

Motivation

The surface mass balance (SMB) of a glacier is a key variable to understand how a glacier responds to local-to-global climate forcing and to model its past or future in response to climate change. Traditionally this SMB is measured at point locations using ablation stakes along with snow depth and density profiles in the accumulation zone. In recent years however, there have been a growing number of approaches developed to derive distributed SMB fields of mountain glaciers by leveraging high-resolution elevation-change, surface ice flow velocity, and ice thickness datasets. These approaches use the mass continuity equation and ice flow parameterizations to calculate the ice flux divergence, constraining the calculation with surface velocity and ice thickness datasets (Cuffey & Paterson, 2010). The flux divergence field is then subtracted from the map of observed elevation changes to obtain, with some density assumptions, the climatic mass balance; in most cases internal and basal mass balance are small, so this is assumed to equate to the SMB. Such approaches are promising as they give an independent SMB estimate solely from remote sensing products relying neither on climatic observations nor on in situ measurements of SMB. For glaciers with in situ measurements, such approaches enable to fill the spatial gaps between these point measurements by bridging the glaciological and the geodetic mass balance approaches.

The scientific applications of these continuity approaches are therefore extremely varied. At present, they have been used to (1) disentangle the spatial variability of the surface mass balance from glacier thinning data, (2) validate energy-balance models at sites with limited in situ data, (3) derive distributed supraglacial debris thickness or avalanching, (4) characterize the state and near-future of glaciers at the regional and global scale. There is also a growing interest from the modelling community to use these spatially distributed estimates to constrain/calibrate their models.

Based on this growing body of literature (e.g. Reynaud et al, 1986; Hubbard et al, 2000; Bisset et al, 2020; Miles et al, 2021; Pelto and Menounos, 2021; Van Tricht et al, 2021; Cook et al, 2023; Zeller et al., 2023 Bhushan et al, 2024; Kneib et al, 2024), there are now several approaches to automatically derive distributed SMB products at a variety of spatial scales. These vary in terms of spatial discretization (flux gates, elevation bands, or 2.5D grids), coverage (full glacier or partial coverage), density assumptions or lack thereof, the degree of flow correction implemented (fluxes only, topographic advection, or a Lagrangian derivation), the quality of input data (local data only vs. regional or global input data products, with various spatiotemporal resolution), the solution strategy (analytical solution, optimization, or ice flow model inversion), and the estimation of associated uncertainties.

As a result, there remain a number of open research questions on the validity and transferability of such estimates: What are the challenges, pros and cons of the different approaches, for instance of Eulerian vs. Lagrangian approaches, or of fully distributed vs. fluxgate approaches? What is the best way to obtain realistic flux divergence fields while enforcing mass conservation? What temporal resolution can these approaches achieve and with what uncertainty, with and without in-situ observations? Which inputs (quality, contemporaneity) or assumptions (characteristic ice thickness, density, basal sliding, basal mass balance) are the biggest sources of uncertainty? Are the estimated uncertainties reasonable in comparison to field data?

We think that some of these questions should be addressed by a community effort that would leverage the expertise of the different groups working on this topic, through an intercomparison exercise of the different approaches developed until now. Such a community effort would be able to deliver clear guidelines and recommendations for future developers and users of SMB products derived from mass continuity approaches.

Figure 1: Depiction of principal steps in workflow for calculating ice flux and specific mass balance on a pixel basis. The glacier depicted is Glacier 354 (reoriented). Figure from Miles et al. (2021).

Objectives

The overall goal of this working group (WG) is bringing together the research community that is developing methodologies to invert glacier surface mass balance from remote sensing datasets. The WG is organized in three work packages (WPs) as follows; the precise breakdown of WP2 and WP3 structured experiments is tentative and will be finalized at the workshops in Q4 2025:

WP1. Led by Maaike Izeboud and Albin Wells. Compiling a benchmark collection of high-quality contemporaneous ice thickness, surface elevation changes, ice surface velocity, and in situ surface mass balance data for several glaciers around the world, along with uncertainty estimates and metadata for each dataset. We will specifically target glaciers with different hypsometric, dynamic and climatic characteristics. We will additionally prescribe several synthetic input datasets of SMB, velocity, thickness, and glacier properties by running a forward ice dynamics model, allowing test cases where all inputs are ‘known’.

WP2. Led by Johannes Fürst and Marin Kneib. Conducting a structured intercomparison experiment to understand the differences between approaches and the importance of key methodological aspects, using the best possible datasets (compiled in WP1). Amongst others, methodological aspects to be tested may include spatial representation (flux gates, spatially distributed), an Eulerian or Lagrangian description, the effect of input regularization and smoothing, and assumptions related to density terms. This experiment will serve as a reference to guide best practices for future methodological developments, and will evaluate whether the models’ individual SMB uncertainty and collective spreads in SMB correspond to the mismatch to observations.

WP3. Led by Evan Miles and Victor Devaux-Chupin. Conducting a structured intercomparison experiment to understand the effectiveness of published approaches to reproduce in situ measurements with reduced qualities of input data. Key aspects to be investigated include the impact of input resolution and errors or gaps in input datasets, by using degraded datasets from WP1 and lower-quality (e.g. widely available thinning, velocity, and thickness) input datasets. The comparison will focus on impacts to output accuracy (correspondence of estimated SMB and uncertainty to in situ measurements) of using gappy, erroneous, or coarse data. This experiment will serve as a reference to guide understanding of the data input requirements for established methods, which inputs are most impactful for continuity inversion results.

Deliverables and milestones

  • Q3 2025: Official announcement of WG and initial workshops for different WPs
  • Q3 2026: Submission of reference glacier dataset
  • Q3 2026: Call for participation in intercomparison experiment
  • Q2 2027: Workshop to discuss results and interpretation
  • Q4 2028: Manuscript submission
  • Q2 2029: Final results presented at IACS assembly

We will also organize periodical conference sessions, e.g. at EGU, AGU, IUGG and IACS assemblies.

Participation

The WG proposal was initiated following an email to Cryolist in April 2025. This proposal was prepared by the co-chairs, representing a group of scientists currently active in developing methods to use the continuity approach (i.e. conversion of mass) to infer surface mass balance from remote sensing observations. The proposal also benefited from a number of bilateral discussions and inputs from several other scientists who expressed interest in this exercise.

Several researchers have already expressed their interest in participating in the WG following an email to Cryolist in April 2025 and through advertisement at the Alpine Glaciology Meeting and the European Geosciences Union in 2025 (Appendix). Once the WG is approved and announced by IACS we will issue another open call on cryolist. Membership is open and we aim for a broad international group of members for all work packages, as well as for additional related activities. If you are interested in joining the WG, please contact the co-chairs including a brief summary of how you intend to contribute to the WG’s goals and which work package(s) you would like to join.

Working Group Membership is open to everybody who has expertise in and is working actively on this topic. To keep the group focused, all WG Members are expected to:
a) actively contribute to at least one of the WG objectives as described above, or to develop a new work package, and
b) participate in annual WG meetings (typically held at major conferences; remote participation is possible) and, in periodic teleconferences in between as necessary.

Open data sharing and transparency are key elements of this Working Group. Thus, WG members are expected to make any relevant new data and insights available to the Members of relevant Work Packages, to support the work packages completion; this requires trust with regards to data use restrictions. Thus, to foster the spirit of collaboration, we advocate for transparency with respect to research priorities and interests. In addition, it should be noted that all data (both the benchmarks of WP1 and the model results from WP2 and WP3) are expected to be submitted to an international data repository.

References

Bhushan, Shashank, David Shean, Jyun-Yi Michelle Hu, Grégoire Guillet, and David Robert Rounce. 2024. “Deriving Seasonal and Annual Surface Mass Balance for Debris-Covered Glaciers from Flow-Corrected Satellite Stereo DEM Time Series.” Journal of Glaciology, September, 1–21. https://doi.org/10.1017/jog.2024.57

Cook, Samuel J., Guillaume Jouvet, Romain Millan, Antoine Rabatel, Harry Zekollari, and Inés Dussaillant. 2023. “Committed Ice Loss in the European Alps Until 2050 Using a Deep‐Learning‐Aided 3D Ice‐Flow Model With Data Assimilation.” Geophysical Research Letters 50 (23): e2023GL105029. https://doi.org/10.1029/2023GL105029

Cuffey, Kurt M., and W. S. B. Paterson. 2010. The Physics of Glaciers. Fourth edition. San Diego: Elsevier.

Hubbard, Alun, Ian Willis, Martin Sharp, Douglas Mair, Peter Nienow, Bryn Hubbard, and Heinz Blatter. 2000. “Glacier Mass-Balance Determination by Remote Sensing and High-Resolution Modelling.” Journal of Glaciology 46 (154): 491–98. https://doi.org/10.3189/172756500781833016

Bisset, Rosie R., Amaury Dehecq, Daniel N. Goldberg, Matthias Huss, Robert G. Bingham, and Noel Gourmelen. 2020. “Reversed Surface-Mass-Balance Gradients on Himalayan Debris-Covered Glaciers Inferred from Remote Sensing.” Remote Sensing 12 (10): 1563. https://doi.org/10.3390/rs12101563

Kneib, Marin, Amaury Dehecq, Adrien Gilbert, Auguste Basset, Evan S. Miles, Guillaume Jouvet, Bruno Jourdain, et al. 2024. “Distributed Surface Mass Balance of an Avalanche-Fed Glacier.” The Cryosphere 18 (12): 5965–83. https://doi.org/10.5194/tc-18-5965-2024

Miles, Evan, Michael McCarthy, Amaury Dehecq, Marin Kneib, Stefan Fugger, and Francesca Pellicciotti. 2021. “Health and Sustainability of Glaciers in High Mountain Asia.” Nature Communications 12 (1): 2868. https://doi.org/10.1038/s41467-021-23073-4

Pelto, Ben M., and Brian Menounos. 2021. “Surface Mass-Balance Gradients From Elevation and Ice Flux Data in the Columbia Basin, Canada.” Frontiers in Earth Science 9 (July):675681. https://doi.org/10.3389/feart.2021.675681

Reynaud, Louis, Michel Vallon, and Anne Letreguilly. 1986. “Mass-Balance Measurements: Problems and Two New Methods of Determining Variations.” Journal of Glaciology 32 (112): 446–54. https://doi.org/10.3189/S0022143000012168

Van Tricht, Lander, Philippe Huybrechts, Jonas Van Breedam, Alexander Vanhulle, Kristof Van Oost, and Harry Zekollari. 2021. “Estimating Surface Mass Balance Patterns from Unoccupied Aerial Vehicle Measurements in the Ablation Area of the Morteratsch–Pers Glacier Complex (Switzerland).” The Cryosphere 15 (9): 4445–64. https://doi.org/10.5194/tc-15-4445-2021

Zeller, L., McGrath, D., Sass, L., O’Neel, S., McNeil, C., & Baker, E. (2023). “Beyond glacier-wide mass balances: parsing seasonal elevation change into spatially resolved patterns of accumulation and ablation at Wolverine Glacier, Alaska.” Journal of Glaciology, 69(273), 87–102. https://doi.org/10.1017/jog.2022.46