Objective
Over the past four decades the Arctic sea ice cover has undergone dramatic changes as a result of anthropogenic CO2 emissions, with precipitous declines in both sea ice thickness and area. Such changes have direct consequences for global climate and human populations, through impacts Ono mid-latitude weather, large-scale ocean circulation patterns and high-latitude climate feedbacks with regulate global-mean temperature. Our ability to quantify the impacts of continued sea ice loss on society and the environment depends on the accuracy with which climate models simulate the coupled interactions between the atmosphere, ocean, and sea ice. However, structural errors associated with the calibration of model physics parameters lead to systematic biases and uncertainty in future projections. For sea ice, one major source of parameter uncertainty comes from the representation of snow thermal conductivity. DAMPEn will demonstrate a unique and multi-disciplinary framework for deriving state-dependent parameter estimates of snow thermal conductivity in the large-scale sea ice model, SI3. For the first time, this framework will allow the estimation of spatially and temporally varying sea ice model parameters. This will be achieved by a three-step process: (1) Estimate snow conductivity parameter corrections via the assimilation of sate-of-the-art sea ice thickness and snow depth data into SI3. (2) Use convolutional neural networks to learn functional relationships between model state variables and snow conductivity corrections. (3) Replace the default (static) snow conductivity parameter in SI3 with the machine learning representation. This data-driven scheme is expected to reduce multi-decadal sea ice biases and uncertainty in future projections. Furthermore, it will provide the groundwork for future developments into the calibration of numerous sea ice model parameters, reducing the cost and carbon footprint associated with climate model calibration activities.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences artificial intelligence machine learning
- natural sciences computer and information sciences artificial intelligence computational intelligence
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2025-PF
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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.
WC1E 6BT LONDON
United Kingdom
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.