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Past and Future High-resolution Global Glacier Mass Changes

Project description

New ways to assess impact of shrinking glaciers

The world’s glaciers continue to lose mass, causing sea-level rise, alterations in river runoff, changes in freshwater influx to the oceans, glacier-related hazards, and shifts in landscapes, all of which impact human livelihoods and ecosystems. Accurate assessments of past, current, and future glacier mass variations are essential for effective adaptation. However, previous reconstructions and projections relied on limited data and oversimplified models, hindered by computational constraints. With this in mind, the ERC-funded GLACMASS project will merge global-scale satellite-derived observations with data assimilation and modern machine learning techniques to deliver a physically-based modelling framework. This innovative approach allows for reliable multi-decadal reconstructions, policy-relevant multi-century projections, and real-time mass change monitoring for selected regions.

Objective

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.

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2022-ADG

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Host institution

UNIVERSITETET I OSLO
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 2 499 957,00
Address
PROBLEMVEIEN 5-7
0313 Oslo
Norway

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Region
Norge Oslo og Viken Oslo
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 2 499 957,00

Beneficiaries (1)

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