Please note that this session was withdrawn and is no longer available in the respective programme. This withdrawal might have been the result of a merge with another session.
CR1.2 | Observing and modelling glaciers at regional to global scales
Observing and modelling glaciers at regional to global scales
Convener: Lilian SchusterECSECS | Co-conveners: Fanny BrunECSECS, Martina Barandun, Johannes J. Fürst, Daniel Farinotti
The increasing availability of remotely sensed observations and computational capacity, drive modelling and observational glacier studies towards increasingly large spatial scales. These large scales are of particular relevance, as they impact policy decisions and public discourse. In the European Alps, for instance, glacier changes are important from a touristic perspective, while in High Mountain Asia, glaciers are a key in the region’s hydrological cycle. At a global scale, glaciers are among the most important contributors to present-day sea level change.

This session focuses on advances in observing and modelling mountain glaciers and ice caps at the regional to global scale. We invite both observation- and modelling-based contributions that lead to a more complete understanding of glacier changes and dynamics at such scales.

Contributions may include, but are not limited to, the following topics:
- Observation and modelling results revealing previously unappreciated regional differences in glacier changes or in their dynamics.
- Large-scale impact studies, including glacier contribution to sea level change, or changes in water availability from glacierized regions.
- Advances in regional- to global-scale glacier models, e.g. inclusion of physical processes such as ice dynamics, debris-cover effects, glacier calving, or glacier surging.
- Regional to global scale process-studies, based on remote sensing observations or meta-analyses of ground-based data.
- Strategies to facilitate or systematise the information flow of observations into models (e.g. blending/homogenisation of different remote sensing products, machine learning algorithms, inverse techniques, data assimilation).
- Inverse modelling of subglacial characteristics or glacier ice thickness at regional scales.