Abstract ID: 3.11437 | Accepted as Talk | Talk | TBA | TBA

Worldwide glacier retreat outside the two large ice sheets is increasingly tangible and the associated ice-loss has dominated the cryospheric contribution to sea-level change for many decades. This ice loss has also become symbolic for the effects of rising temperatures. In addition to the anticipated importance for future sea-level rise, continuing glacier mass loss will affect seasonal freshwater availability and might add to water-stress in this century in many regions.

Here, we present a self-consistent, ice-dynamic forecasting framework for glacier evolution. For the first time, each glacier on Earth can be treated as a three-dimension body within its surrounding topography without the severe geometric simplifications typical on regional and global scales. The heart of the framework is the systematic utilisation of the rapidly growing body of information from satellite remote sensing. For this purpose, we passed on to ensemble assimilation techniques that transiently consider measurements as they become available – increasing the total information flow into glacier system models. The 3D modelling framework also allows a direct integration of iceberg calving, which is, on regional scales, an important but often unconsidered ice-loss term. Finally, we refined the representation of the local energy balance at the glacier surface improving the multi-decadal stability in the melt formulation.

The performance of the data assimilation was tested on synthetic glacier geometries. For real-world applications, convincing agreement was found against independent measurements. Meanwhile the approach is automated for regional application, ingests remote sensing observations between 2000-2020 and produces a digital representation of any glacier on this planet. This representation is created together with a distinct and associated uncertainty umbrella. The latter is highly valubale, considering the large spectrum of observational coverage and quality in various mountain regions. In summary, we are convinced that our framework will improve our capabilities to represent glacier systems when pursuing regional to global-scale simulations – especially in regional with limited in-situ measurements.