Understanding the transport of ocean tracers such as plastic, heat or salt is an important topic in oceanography. If we place a very small particle, e.g. a plastic particle or a droplet of dye, in the ocean and let it flow with the currents, its pathway (or trajectory) typically strongly depends on where and when the particle started – the ocean flow is said to be chaotic. Practically, chaos means that it is very difficult to predict a particle’s pathway, for example with computer simulations. However, if we look at the behavior of large groups of particles rather than individuals, there is often some kind of large-scale order that is more or less robust to the chaotic motion of individual particles. The focus of this thesis is the extraction of such structure from a large amount of particle trajectories.
An example of such a large-scale phenomenon in the ocean are ‘eddies’, which are swirls of water that capture water masses and can transport them over hundreds of kilometers. Another example is the accumulation of plastic in the center of the North Pacific Ocean. In this thesis, we show how existing concepts from mathematics and data sciences can be applied in the ocean context in order to extract and analyze these large-scale features from chaotic particle trajectory data. The results of this thesis shed some light on the potential that data-driven methods could have for future research in ocean tracer transport.