Internal waves and eddies are important drivers of ocean mixing and so fundamentally linked to large-scale ocean circulation and regulation of the Earth’s climate. However, the dynamic nature of these features means they are tricky to observe in-situ using traditional oceanographic methods. Autonomous ocean gliders are a new and exciting solution to this problem with the potential to fill the time-space sampling gap between hydrographic mooring timeseries and semi-synoptic ship surveys (Sherwin et al. 2015).
Gliders are excellent platforms for observing variability in ocean circulation over prolonged periods. However, smaller dynamical features such as internal waves and eddies present a problem because: (a) their spatial scale is of the same order as the horizontal distance between glider dives and (b) they propagate at speeds comparable to the gliders themselves. Along a section, temporal variability is projected onto spatial variability due to Doppler smearing and aliasing (Rudnick and Cole 2011). Similarly, if imperfectly occupying a station, spatial variability is projected onto temporal variability. Full understanding of this problem is critical for robust interpretation of existing glider datasets and the development of internal wave and eddy resolving sampling schemes will allow future missions to elucidate the dynamics of these important ocean processes.
This PhD project will investigate how gliders can be used to accurately sample these important yet elusive dynamic features of ocean circulation, focusing on processes that occur in the subpolar North Atlantic, an eddy-rich an tidally energetic region. The student will: (a) use idealised and realistic ocean models to assess the skill of different sampling schemes and mission profiles (e.g., glider speed; dive slope; sections vs. stations; configuration of multiple gliders) in resolving internal waves and eddies; (b) develop methodologies for identifying and these features in observational glider datasets and for assessing the uncertainty associated with imperfect spatial-temporal sampling; and (c) use archive and on-going glider data from the subpolar North Atlantic to investigate how these features move and evolve over weeks and months in response to large-scale flows and seasonal changes in stratification. The student will be linked in to the Overturning in the Subpolar North Atlantic Program (OSNAP) and Extended Ellett Line project at SAMS. Through these programmes, SAMS has archived glider data and on-going continuous glider presence in the sub-polar North Atlantic.
The NEXUSS CDT provides state-of-the-art, highly experiential training in the application and development of cutting-edge Smart and Autonomous Observing Systems for the environmental sciences, alongside comprehensive personal and professional development. There will be extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial / government / policy partners. The student will be registered at University of East Anglia (UEA) and hosted at UEA School of Environmental Sciences. Specific training will include:
Autonomous ocean glider data processing, quality control, and analysis techniques;
Numerical modeling of dynamic ocean processes including internal waves and eddies;
Statistical analysis and interpretation methods for model output and glider data;
Ocean glider operations, including preparation, deployment, and piloting;
Participation in oceanographic research cruises;
Presentation of research at international conferences and workshops.
Rudnick, D. L., and S. T Cole, 2011: On sampling the ocean using underwater gliders. Journal of Geophysical Research, 116, C08010, doi:110.1029/2010JC006849.
Sherwin, T. J., D. Aleynik, E. Dumont, and M. E Inall, 2015: Deep drivers of mesoscale circulation in the central Rockall Trough. Ocean Science, 11, 343-359.
Start date October 2015
Studentship Length 3 years 8 months
Acceptable First Degree First degree subjects: Oceanography, marine science, meteorology, geophysics, environmental sciences, physics, mathematics, engineering, any physical science. Minimum Entry Standard: Undergraduate degree with a classification of 2:1 (or international equivalent).