Marta Mrozowska is a Ph.D. candidate at PICE (Physics of Ice, Climate and Earth), under the Niels Bohr Institute, with a specialization in climate physics research. She attained a master’s degree in physical oceanography – a discipline whose existence she was not aware of when she began studying! In physical oceanography, the theory of ’fluid dynamics’ is used to understand and predict the state of the ocean and its currents. Wind power farms, shipping of cargo, fishing, prediction of disastrous phenomena such as tsunamis, flooding, or El Ninõ events – it’s a field relevant to countless industries, and directly to the lives of ordinary people all over the Earth.

Marta’s project deals with ocean models that are used for weather and climate prediction. Most of the methods for remote measurements used today rely on light. However, the ocean is opaque to light, and so collecting data at sea requires a different set of approaches. Global sensing programs utilizing moorings, drifters and manned ships have been around for decades, and they contribute to an ever-expanding database of ocean properties. Even so, the ocean is huge, covering 71% of the planet’s surface, with an average depth of 4km that exceeds 11km at its deepest – and there is incredibly much left to discover! 

What this means is that much of the ocean research today relies on working with numerical models. These serve to bridge the gap between the data we have and the information we need to answer the questions we pose. At the heart of fluid dynamics lies equations which are not yet completely understood, due to a phenomenon called ’turbulence’. The process of turbulence can be approximated in climate models using the so-called ’turbulence closure schemes’. It’s these schemes that Marta’s project serves to evaluate and optimize for a range of ocean models. Her hope is that by the end of her Ph.D., she can make a modest contribution towards unraveling how to best represent these chaotic physical processes in our oceans, which in turn may contribute to the reliability and improvement of long-term climate prediction.