Innovating Works

GLACMASS

Financiado
Past and Future High-resolution Global Glacier Mass Changes
World-wide glaciers are losing mass which affects global sea-level, river runoff, freshwater influx to the oceans, glacier-related hazards, and landscape changes, with implications for human livelihoods and ecosystems. Hence, accu... World-wide glaciers are losing mass which affects global sea-level, river runoff, freshwater influx to the oceans, glacier-related hazards, and landscape changes, with implications for human livelihoods and ecosystems. Hence, accurate estimates of past, current and future glacier mass variations at a high temporal and spatial resolution are key to effective adaptation strategies. However, previous mass-balance reconstructions and projections have relied on scarce observations with limited spatial and/or temporal resolution, as well as overparameterized, insufficiently constrained and highly simplified models, the latter necessitated by high computational costs incurred by the global scale. GLACMASS will propel the current state-of-the-art of global-scale glacier reconstruction and projection forward in unprecedented ways by delivering a fundamentally novel and internally consistent physically-based modelling framework that draws, for the first time on a global scale, on both data assimilation and modern machine learning techniques facilitated by emerging global-scale glacier-related satellite-derived data. The framework will be used to reconstruct multi-decadal past glacier changes, and make policy-relevant multi-century projections of mass and area changes of all >200,000 glaciers outside the ice sheets with unprecedented accuracy, spatiotemporal detail and computational efficiency, and also nowcast present mass changes in a near-real-time fashion for selected regions. The model framework will fuse output from a novel physically-based glacier evolution model with all relevant observations available for each glacier, such as in-situ, geodetic and gravimetry-derived mass balances, as well as snowlines and other observations, thus simultaneously exploiting the untapped strengths of different types of observational data sets in an optimal manner. ver más
30/09/2028
2M€
Duración del proyecto: 64 meses Fecha Inicio: 2023-05-06
Fecha Fin: 2028-09-30

Línea de financiación: concedida

El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-05-06
Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:
ERC-2022-ADG: ERC ADVANCED GRANTS
Cerrada hace 2 años
Presupuesto El presupuesto total del proyecto asciende a 2M€
Líder del proyecto
Innovasjon Norge No se ha especificado una descripción o un objeto social para esta compañía.