Using deep learning to map sea level and ocean dynamics along the North Atlantic eastern boundary

Rising seas due to melting ice sheets and ocean warming pose a major threat to the large fraction of the world’s population living in low-lying coastal regions. As the climate warms, large-scale adjustments in the ocean’s circulation may also drive abrupt changes in coastal sea level, locally amplifying or ameliorating global mean sea level rise and its impacts. Therefore, while much sea level research has centred around its global mean value, there is a growing realisation the we must better understand the factors, such as the ocean’s circulation, that drive regional variations in sea level. For example, ocean models suggest that sea level falls by about 60 cm along the eastern boundary of the North Atlantic between the equator and Northern Europe. However, due to measurement limitations, accurate observations of this change in coastal sea level, or the dynamics by which it is maintain, have yet to be made. Machine learning provides an opportunity for fundamental progress to be made in this exciting field. The ultimate aim of this project is to identify the ocean processes sustaining the large drop in sea level along the eastern boundary of the North Atlantic, thereby explaining a poorly understood feature of the ocean’s large-scale circulation with implications for sea level rise. This goal is hampered, however, by three primary difficulties: (i) obtaining a clean sea level signal in coastal zones using satellite altimetry; (ii) removing the much larger gravity signal from the SSH to obtain the small (~1%) residual related to ocean dynamics and (iii) relating the surface signal to sparse sub-surface observations to identify the boundary dynamics. Here we propose to employ machine learning techniques to overcome these difficulties. This project will be conducted in collaboration with the National Space Institute at the Danish Technical University (DTU), who are world leaders in geodesy and satellite altimetry, particularly in coastal regions where the measurement of sea level from space is the most challenging. The student will spend at least one month per year at DTU in Copenhagen working on the altimetric aspect of the project.