ML Transport (101163887)

  https://cordis.europa.eu/project/id/101163887

  Horizon Europe (2021-2027)

  Observing, Modeling, and Parametrizing Oceanic Mixed Layer Transport Processes

  ERC STARTING GRANTS (ERC-2024-STG)

  physical oceanography

  2025-01-01 Start Date (YY-MM-DD)

  2029-12-31 End Date (YY-MM-DD)

  € 2,422,688 Total Cost

  Description

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.

  Complicit Organisations

1 Israeli organisation participates in ML Transport.