Supervisors:
Claudie Beaulieu (lead, UoS), Dorothee Bakker (UEA), Sujit Sahu (UoS)
Over half of total CO2 emissions is absorbed by land and ocean sinks, thus slowing the accumulation in the atmosphere. The Southern Ocean plays a key role, responsible for about half of all CO2 absorbed by the global ocean sink. The Southern Ocean sink of carbon has reportedly weakened [1], with important implications including a continuing reduction of the efficiency of this sink and possibly a substantial impact on the rate of climate change.
In order to assess temporal changes in the ocean sink, trends in the partial pressure of CO2 (pCO2) have been examined using different methods and over varying intervals, leading to diverse interpretations [2]. New data from rapidly developing sensor technologies and ongoing observing programs have the potential to reconcile previous studies and constrain the uncertainty of observed pCO2 trends in the Southern Ocean.
The main objective of this project is to take full advantage of all available data (e.g. cruise, floats) by combining them in a statistical space-time model that will be used to infer spatial and temporal changes in ocean pCO2, and provide a full treatment of their uncertainty.
The student will use the most recent version of sea surface pCO2 observations from the database Surface Ocean CO2 Atlas (SOCAT) [3]. To date, this database constitutes the largest source of information for pCO2 in the Southern Ocean. Additional recent observations from floats through the Southern Ocean Carbon and Climate Observations and Modelling project (SOCCOM) project ( http://soccom.princeton.edu/ ) and the more recent Ocean regulation of Climate through Heat and Carbon Sequestration and Transports (ORCHESTRA) program will also be used.
The student will first develop a statistical model representing the space-time structure of ocean pCO2 and its error for each type of measurement. Then, these representations will be combined into a hierarchical space-time model [4]. Using the statistical space-time model developed, the student will estimate temporal trends in ocean pCO2 and their uncertainties. A Bayesian approach will allow the uncertainty to be characterized and the full probability distribution of the trends to be assessed, and therefore permit a likelihood assessment for the presence of long-term trends using the IPCC nomenclature (e.g. virtually certain (>99%), extremely likely (>95%), very likely (>90%), likely (>66%), etc.).
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 Southampton, and hosted at Ocean and Earth Science. Specific training will include:
The student will learn about space-time modelling, Bayesian analysis, relevant aspects of ocean biogeochemistry and its interaction with climate. The student will receive training in methods of research by the NOC Graduate School and the Southampton Statistical Sciences Research Institute (S3RI), where he/she will attend appropriate university Masters level lectures to gain relevant background knowledge. In addition, the student will also benefit from research led short courses on Bayesian statistics and space-time modelling developed by Sahu and hosted by S3RI. Presentation of the results at national and international conferences will be expected and encouraged.
Background Reading:
Le Quéré et al. (2007) Saturation of the Southern Ocean CO2 sink due to recent climate change. Science, 316, 1735-1738.
Landschützer et al. (2015) The reinvigoration of the Southern Ocean carbon sink. Science, 349, 1221-1224.
Bakker et al. (2016) A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT). Earth System Science Data Discussions, doi:10.5194/essd-2016-15.
Bakar and Sahu (2015) spTimer: Spatio-Temporal Bayesian Modeling Using R. Journal of Statistical Software, 63(15).
To apply for this project, use the: apply for a NEXUSS CDT studentship