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Observing, Modeling, and Parametrizing Oceanic Mixed Layer Transport Processes

Project description

Unlocking ocean transport dynamics

Understanding how oceans regulate the transport of heat, nutrients, carbon, and pollutants is vital for predicting climate change and mitigating environmental risks. However, the near-surface mixed layer dynamics remain poorly quantified, partly due to the high variability and complexity of sub-mesoscale currents like fronts, filaments, and eddies. These intricate flows strongly influence vertical exchange processes, yet are challenging to observe directly and remain inadequately represented in climate models. With this in mind, the ERC-funded ML Transport project aims to close this critical knowledge gap. By combining cutting-edge simulations, theoretical frameworks, and field campaigns, it will provide essential insights into ML physics, improving ocean modelling and guiding solutions for pollutant mitigation and climate uncertainty.

Objective

Although crucial in determining the oceanic distribution of heat, biological nutrients, carbon, and pollutants, the circulation dynamics that governs oceanic vertical transport processes of physical and biogeochemical properties through the near surface layer (i.e. mixed-layer; ML) are yet to be comprehensively quantified. Recently we and others have demonstrated that submesoscale currents (SMCs) -- newly discovered flow structures consisting of fronts, filaments, and eddies -- have a strong influence on these exchange processes, to be fully explored and characterized.
The high spatiotemporal variability of SMCs and the complex physics that determines their interactions with other ML phenomena like surface gravity waves and near-inertial waves, renders in situ measurements of these processes extremely difficult to obtain. Furthermore, current climate models lack the grid resolution and variable forcing components required to adequately represent ML physics, making it one of the greatest uncertainties in climate projections.
The proposed research will address this critical gap through three objectives: 1) develop the numerical capability to simulate ML physics in a realistic inhomogeneous environment while resolving boundary layer turbulence and wave dynamics; 2) develop a theoretical framework for a physics-based parametrization of ML vertical exchange rates; and 3) directly measure turbulent mixing, tracer distribution, and transport rates near SMCs in situ, providing the crucial observational support necessary to guide and fine-tune the parameterizations.
To achieve this goal, we will extend our frontogenesis theory, analyze particle and tracer concentrations in carefully designed realistic and nested large-eddy simulations, and examine drifter trajectories and passive tracer spreading in multi-asset field campaigns.
This synergistic approach will substantially impact oceanic biogeochemical modeling, pollutant transport mitigation, and climate projections.

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Keywords

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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-2024-STG

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Host institution

TEL AVIV UNIVERSITY
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.

€ 2 422 688,00
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.

€ 2 422 688,00

Beneficiaries (1)

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