PhD defence: From mesoscale to microscale: Estimating ocean mixing and stirring from observations

Mixing in the ocean consists of many different processes that act on a large range of spatio-temporal scales, from mm to over 100 km, and from seconds to months. This way, these processes affect how the ocean absorbs, transports and stores tracers, such as heat and carbon, and thereby our climate. The way how these mixing processes stir and mix the waters also has implications for larger systems, such as, but not limited to, the extend of Oxygen Minimum Zones, hydrological cycles, sea-surface temperature and sea-level rise. To increase our understanding of the climate and projections of its future changes, a better understanding of the magnitude and causes of these mixing processes is needed.

 

However, both in modelling and (observational) studies of ocean and climate processes, the mixing occurs at scales that cannot be accurately resolved. Either because the model grid is too coarse, or the scales at which data is collected are insufficient. Therefore we rely on parameterizations: approximations that describe the effects of the mixing based on larger scale variables. These parameterizations are sensitive to unconstrained choices, but they can be improved by observationally-based constraints.

 

This thesis explores various ways to obtain estimates of the ocean mixing strength from observational data. These mixing estimates will help our understanding of these mixing processes and improve parameterizations of ocean mixing.