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Understanding and Modelling the Earth System with Machine Learning

Project description

Machine learning expands into climate modelling

Earth system models are the basis for understanding and projecting climate change. Despite progress in the field, the models’ ability to simulate both global and regional Earth system responses is limited by the representation of physical and biological small-scale processes. The EU-funded USMILE project will use machine learning to improve modelling and understanding of the Earth system. Researchers will develop machine learning algorithms to enhance Earth observation datasets accounting for spatio-temporal covariations, and machine learning-based parametrisations and sub-models for clouds and land-surface processes that have hindered progress in climate modelling for decades. In addition, they will detect and elucidate modes of climate variability and multivariate extremes and uncover dynamic aspects of the Earth system with novel deep learning and causal discovery techniques.

Objective

Earth system models are fundamental to understand climate change. Although they have improved significantly, considerable biases and uncertainties in their projections remain. Process parameterisations limit the models’ ability to simulate both global and regional Earth system responses, which are key for assessing climate change and its impacts on ecosystems and society. In recent years, the volume of data from high-resolution models and observations has substantially increased to petabyte scales. Concomitantly, the field of machine learning (ML) has quickly developed, promising breakthroughs in detecting and analysing non-linear relationships and patterns in large multivariate datasets. Yet, traditionally, physical modelling and ML have been often treated as two different worlds with opposite scientific paradigms (theory-driven versus data-driven). Thus, despite its great potential, ML has not yet been widely adopted for addressing the urgent need of improved understanding and modelling of the Earth system. USMILE will combine multi-disciplinary expertise in ML and process-based atmosphere and land modelling to completely rethink model development and evaluation. ML will further allow us to define novel observational constraints on Earth system feedbacks and climate projections. We will (1) develop ML algorithms to enhance Earth observation datasets accounting for spatio-temporal covariations, (2) deploy ML-based parameterisations and sub-models for clouds and land-surface processes that have hindered progress in climate modelling for decades, and (3) detect and understand modes of climate variability, multivariate extremes and uncover dynamical aspects of the Earth system with novel deep learning and causal inference techniques. USMILE will drive a paradigm shift in the current modelling of the Earth system towards a new data-driven physics-aware science and to an unprecedented reduction of uncertainties in projections.

Fields of science (EuroSciVoc)

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Keywords

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Programme(s)

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Topic(s)

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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.

ERC-SyG - Synergy grant

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) ERC-2019-SyG

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Host institution

DEUTSCHES ZENTRUM FUR LUFT - UND RAUMFAHRT EV
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.

€ 2 977 864,00
Address
LINDER HOHE
51147 KOLN
Germany

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Region
Nordrhein-Westfalen Köln Köln, Kreisfreie Stadt
Activity type
Research Organisations
Links
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.

€ 2 977 864,00

Beneficiaries (4)

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