Georgy Manucharyan

First AI-generated global SSH map. The future is here!

Satellite observations of sea surface height (SSH) are widely used to derive surface ocean currents on a global scale. However, due to gaps in SSH observations, it remains challenging to retrieve the dynamics of rapidly evolving upper-ocean currents. To overcome this limitation, we propose a Deep Learning framework that is based on pattern recognition extracted from SSH observations. Using synthetic data generated from a simplified model of ocean turbulence, we demonstrate that deep learning can accurately estimate both surface and sub-surface ocean currents, significantly outperforming the most commonly used techniques. By providing a proof of concept, our study highlights the strong potential of deep learning for estimating ocean currents from satellite observations.

Sea ice dynamics is a topic of continuing debate, particularly at scales of motion that are comparable to sea ice floes. We are developing from scratch a sea ice model that is based on an explicit representation of the floe lifecycle, including the fractures, welding, formation, etc. The goal is to have a model that could bridge the gap between the floe scale and the basic scale sea ice dynamics.

Localized and episodic fluxes due to submesoscale oceanic turbulence play an important role in larger scale dynamics and affect biogeochemical cycles via vertical advection of nutrients. We are exploring submesoscale variability in the Arctic Ocean, particularly in marginal and seasonal ice zones, where active mechanical and thermodynamical eddy interactions with overlying sea ice can affect its growth/melt rates.

Mesoscale and submesoscale eddies in marginal ice zones in the Arctic Ocean as simulated by the LLC4320 ocean model.

Submesoscale eddies revealed by sea ice patterns in marginal ice zones

Arctic Ocean stores a large amount of freshwater within the Beaufort Gyre. The freshwater accumulation was linked to changing atmospheric winds, which can cause a release of the accumulated fresh waters with global climate implications. Nonetheless, the basic gyre dynamics are not well understood and it is unclear how much freshwater can be held in under persistent forcing and how quickly it can be released. Combining theory with numerical simulations, we work to explore dominant physical processes governing the FWC dynamics, focusing on the interactions between eddies, mean flow, and sea ice. It is the interplay between these processes that defines the gyre stability and affects its variability.

  

Eddies are commonly observed in the Arctic Ocean and can affect the melting of sea ice due to increased heat fluxes associated with enhanced boundary layer turbulence. However, the nature of mesoscale and submesoscale variability in the Arctic Ocean is not well understood. We use idealized models and high-resolution simulations along with in situ observations to explore the dynamics of outcropping density fronts and identify factors influencing the formation of coherent eddies.