Our climate is affected by turbulent oceanic flows where energy is being continuously transferred across a wide range of scales -- quantitative understanding of these processes remains one of the big challenges in oceanography. My research resides at a synergetic overlap between geophysical fluid dynamics, physical oceanography, and climate dynamics. I am leading the Ocean Dynamics group at UW that works on mesoscale and submesoscale ocean turbulence, sea ice-ocean interactions, floe-scale sea ice modeling, laboratory experiments, remote sensing, as well as on applications of Deep Learning to theoretical problems in ocean turbulence. Our approach is to gain fundamental knowledge by reducing complex phenomena to their driving mechanisms and developing mathematical models to quantify and predict their evolution. Feel free to contact me about potential student and postdoctoral opportunities.
I'm a graduate student exploring mesoscale and submesoscale ocean turbulence using deep learning. My research focuses on using deep learning to more accurately reconstruct gridded sea surface height maps from sparse along-track satellite altimeter observations. By synthesizing approaches from physics and deep learning we hope to learn new lessons about the turbulent dynamics of the upper ocean.
Yang (Kitty) Wang
I'm a graduate student working on understanding energy fluxes in submesoscale ocean turbulence.
I'm a postdoctoral scholar focusing on understanding the interactions between sea ice and submesoscale ocean variability. My research uses ocean-ice models to design idealized high-resolution numerical experiments to explore the underlying physics and devise parameterizations.
I'm a postdoctoral scholar. My research utilizes applied mathematics to investigate the underlying physics of arctic sea ice. I am working on a sea ice model utilizing a discrete element method to resolve interactions and phenomenon at the floe-scale. Additionally, my research will implement asymptotic techniques to explore the coupling of interactions between the disparate length scales exhibited by sea ice.
Former UW undergraduate students
Snehal Shokeen, James Kunetz.
Former summer program students
Peiyun Zhu (2016 Caltech SURF, now at Stanford University),
Jessica Kenigson (2017 GFD Summer School at WHOI, now at U. Colorado Boulder),
Robert Fajber (2017 GFD Summer School at WHOI, now at University of Washington),
Tom George (2018 Caltech SURF, now at University College London)
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 contining 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.
Isotropic floe fractures leading to sea ice motion under external stresses
Nares Strait simulation with ice floes fracturing as they enter the bottleneck
Winter-like sea ice dynamics with ice floes fracturing, forming, and welding
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 submesocale 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.
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 I work to explore the 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.