Summary
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.
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.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101096057 |
| Start date: | 01-10-2023 |
| End date: | 30-09-2028 |
| Total budget - Public funding: | 2 499 957,00 Euro - 2 499 957,00 Euro |
Cordis data
Original description
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.
Status
SIGNEDCall topic
ERC-2022-ADGUpdate Date
31-07-2023
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