Our group works on trying to understand physical and biogeochemical processes in the ocean interior.

• Studying what is happening below the ocean surface is quite difficult! Global ocean and climate models can't quite capture the small-scale processes that occur on millimetre to centimetre scale. In order for them to do so, they would need one thousand billion times higher resolution than currently possible. Direct measurements from ships or autonomous instruments can be quite expensive and difficult, so they are sparesely available.

• We use computational fluid dynamics methods and machine learning to study specific processes occuring in the ocean to improve our understanding of these processes and fill in observational gaps. 

 1)  Ocean currents interacting with rough boundary:

When ocean currents encounter roughness along a boundary, internal waves are generated. These waves are oscillations within the ocean interior. Just like surface waves break at the beach, internal waves can break (but below the surface) that creates turbulence. Boundary roughness can be along the ocean bottom boundary (for example, small hills) or along the ocean top boundary (for example, ridges along the bottom of sea ice). These processes occur on small scales and aren't well-captured in climate models.

Our group works on understanding the fundamental physics of these processes (internal wave generation, turbulent dissipation) by conducting numerical simulations. 

                                                                                        Related publications: Zemskova and Grisouard, 2021, 2022; De Abreau et al, 2024

2) Estimating ocean carbon storage trends:

The ocean is estimated to absorb 25% of the anthropogenic carbon emissions from the atmosphere. However, there are large uncertainties about changes in ocean's potential to take up more carbon. Large-scale ocean and climate models have to rely on approximations of small-scale processes and simplifications of ocean biogeochemistry. Observational measurements are spatially and temporally sparse.

Our group works on applying machine learning techniques to: (1) create high spatial and temporal resolution datasets of ocean dissolved carbon concentrations, and (2) understand mechanisms driving the changes in ocean carbon concentrations.

Related publications: Zemskova et al, 2022

3) Internal wave generation at the coast:

Internal waves can be generated in regions of sloping topography near the coast, where the ocean depth rapidly changes from the deep ocean (~4 km) to coastal shelves (~100 m). A portion of these waves travels back into the deep ocean away from the coast, and eventually breaks contributing to turbulent processes in the ocean interior. Another portion stays on to travel along the coastal shelves, which has importan implications for coastal communities and industries, such as fisheries and harbours.

Our group studies how differences in the coastal geometry affect how much of the internal wave energy is radiated away from the coast compared to how much stays on the coast using numerical simulations.

Related publications: Zemskova et al, 2024; Musgrave et al, 2024