Global mean sea level (GMSL) has been rising about 2 times faster in the past 30 years than throughout most of the 20th century due to increased land-ice melting and ocean thermal expansion (1), significantly increasing the risk of coastal flooding and erosion in countries across the globe. While the rate of GMSL rise and the contribution from its individual sources are relatively well constrained, particularly since the 1990s, understanding how this rise in GMSL relates to a rise in coastal sea level remains a key scientific challenge. This is critical as the local rate of sea-level rise can deviate significantly from that of GMSL.
Theoretical studies suggest that open ocean and coastal sea level can be largely decoupled due to the insulating effect by the continental slope (2). Such theoretical findings have been corroborated by observational and model studies, which have found that coastal sea-level variations are often coherent over large distance along the coast but decoupled from nearby deep-ocean changes (3,4). While there is broad scientific consensus that coastal and deep-ocean sea level can differ, the question of how and under what conditions open ocean sea-level changes are communicated to the coast remains unanswered. Addressing this question requires observations of sea level extending from the coast, through the shelf, to the deep ocean. Until recently, satellite altimetry observations were unreliable in the coastal zone, preventing us from making the connection between the coast and the deep ocean. However, recent advances in altimetry retracking, such as the introduction of the ALES retracker (5), as well as the launch of new generation SAR altimeters (6), such as CryoSat-2 and Sentinel-3, raise now the possibility of addressing this issue.
The aim of this project is to combine tide gauge and coastal/SAR altimetry data through novel Bayesian hierarchical methods to gain new insights into the connection between open ocean and coastal sea-level changes.
- How does the transmission of oceanic signals to the coast depend on the spatial and temporal scales of the signal?
- How do the characteristics of edge shelf variability influence this transmission?
- How does this transmission differ between eastern and western ocean boundaries?
A spatiotemporal Bayesian hierarchical model combining tide gauge and coastal/SAR altimetry data will be developed to separate the sea-level trend and shorter-term variability and characterize their spatial scales along the eastern and western boundaries of the North Atlantic. The coastal altimetry data will be based on the ALES retracker developed at NOC (5) whereas the SAR data will include data from CryoSat-2, Sentinel-3 and Sentinel-6. Prior information from state-of-the-art ocean models, such as the NEMO 1/12 degree, will be incorporated in the Bayesian model to improve estimation. Recent work at NOC has used Bayesian models to investigate sea-level extremes in Europe (7) and this, together with the pioneering work by Edinburgh on spatial statistics, will provide a solid foundation for the project. The Bayesian model estimates will be analysed to explain the connection between open ocean and coastal sea level, with focus on the role of edge shelf variability.
Year 1: Research training. Familiarization with sea-level data and Bayesian methods.
Year 2: Development of the Bayesian hierarchical model.
Year 3: Analysis of the Bayesian model solution to address the research questions above.
Supervisors: Francisco M. Calafat (NOC), Christine Gommenginger (NOC), and Finn Lindgren (University of Edinburgh). The student will be based at NOC Liverpool, but will be registered for their PhD at the University of Edinburgh’s School of Mathematics, which they will visit 1-2 times per year. They will also take part in training at the Universities of Edinburgh and Leeds, and at NOC in Southampton, along with other SENSE PhD students.
This PhD is part of the NERC and UK Space Agency funded Centre for Doctoral Training "SENSE": the Centre for Satellite Data in Environmental Science. SENSE will train 50 PhD students to tackle cross-disciplinary environmental problems by applying the latest data science techniques to satellite data. All our students will receive extensive training on satellite data and AI/Machine Learning, as well as attending a field course on drones, and residential courses hosted by the Satellite Applications Catapult (Harwell), and ESA (Rome). All students will experience extensive training on professional skills, including spending 3 months on an industry placement. See http://www.eo-cdt.org
1. Church, J. A. et al., Climate change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge; New York, pp. 1137–1216.
2. Huthnance,J. M. (2004) Ocean-to-shelf signal transmission: A parameter study,JGR, 109, C12029.
3. Calafat, F. M., D. P. Chambers, M. N. Tsimplis (2012). Mechanisms of decadal sea level variability in the eastern North Atlantic and the Mediterranean Sea, J. Geophys. Res., 117, C09022.
4. Calafat, F. M., T. Wahl, F. Lindsten, J. Williams, E. Frajka-Williams (2018). Coherent modulation of the sea-level annual cycle in the United States by Atlantic Rossby waves, Nat. Commun., 9, 2571.
5. Passaro, M., P. Cipollini, S. Vignudelli, G. Quartly, and H. Snaith (2014), ALES: A multi‐mission subwaveform retracker for coastal and open ocean altimetry, Remote Sens. Environ., 145, 173–189.
6. Cipollini, P., F. M. Calafat, S. Jevrejeva, A. Melet, P. Prandi (2017). Monitoring Sea Level in the Coastal Zone with Satellite Altimetry and Tide Gauges, Surv. Geophys., 38, 33-57.
7. Calafat, F. M., M. Marcos (2019). Probabilistic reanalysis of storm surge extremes in Europe, Proc. Natl. Acad. Sci. U. S. A., accepted.