PICO
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. Glacier play a key role in current sea-level contribution, in seasonal water availability, in the susceptibility to natural hazards or for touristic activities. To tackle the spatial challenge, AI informed techniques became of particular interest in terms of computational feasibility both for data analysis and model forecasting.
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, which may include, but are not limited to the following topics:
• comparative studies of glacier evolution across single or multiple mountain ranges
• glacier-related impact studies on sea-level contribution, mountain hazards, mountain hydrology, etc.
• advances in large-scale monitoring
(e.g., AI-supported monitoring, multi-sensor homogenisation, meta-analysis of ground-based data, process inferences)
• advances in large-scale modelling
(e.g., reconciling AI with classical approaches, including physical processes, model coupling to others subsystems, improving strategies for data assimilation, refining climatic downscaling)
• regional to global-scale data products and scalable modelling frameworks