Turbulence in the ocean surface boundary layer (OSBL) directly controls transport and dispersion of passive materials near the ocean surface. Many such materials directly affect human and ecosystem health and human activities (e.g. microplastic, oil and nutrients and phytoplankton) as well as climate-change-relevant global budgets (e.g. carbon dioxide), making their mixing and transport mechanisms an important topic of investigation. However, the dynamics of the upper ocean are such that most of the processes and flow structures that are relevant to such transport are too small to be investigated with global circulation models and challenging to be investigated with field and laboratory experiments. As a result much is unknown about how the transport and mixing of materials is affected by OSBL turbulence in different regimes, particularly when surface waves are present, which have no analog in atmospheric boundary layers.

Based on these considerations, this dissertation is dedicated to the investigation of passive material transport and mixing in OSBLs under generalized regimes (i.e. regimes that are a combination of convection, wind-stress-driven turbulence and wave-driven turbulence). Focus is given to small-scale processes (smaller than an OSBL depth) and the tool of choice is the large-eddy simulation (LES) technique, which can resolve the relevant eddies for transport while parameterizing smaller ones.

One of the contributions of this dissertation is the exploration of a wide range of upper ocean regimes and its analysis over a general framework. This is challenging since the dynamics of these regimes can be extremely different from each other, which reflects on material transport as extremely different outcomes for different regimes. Furthermore, another major contribution is the development of a method to aid in the development of eddy-diffusivity models for OSBL mixing. While development of such models has historically depended on choices of scaling laws and shape functions that have to be made \textit{a priori} (and therefore constrain the outcome), the method developed here does not make any such assumptions. Thus, all results naturally emerge from data. Finally, after showing that results conform to current knowledge of upper ocean physics, we modify existing models accordingly and show that results are improved with the adopted modifications.