Dr Jacqueline Christmas, Department of Computer Science, College of Engineering, Mathematics and Physical Sciences, University of Exeter
Location: University of Exeter, Streatham Campus, Exeter EX4 4QJ
This project is one of a number that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP). The GW4+ DTP consists of the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus five Research Organisation partners: British Antarctic Survey, British Geological Survey, Centre for Ecology and Hydrology, the Natural History Museum and Plymouth Marine Laboratory. The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/
Human activity is changing the environment of our planet, including the carbon cycle. The carbon dioxide (CO2) content of the atmosphere is increasing, leading to changes in the terrestrial and marine carbon cycles (Le Quéré et al., 2017).
Knowledge of the ocean uptake of CO2, with a high degree of confidence, is crucial in determining the current state of the carbon cycle in the marine environment, as well as improving the certainty in results from predictive models (Intergovernmental Panel on Climate Change, 2013).
Additionally, the biogeochemical drivers of variability of the air-to-sea CO2 exchange need to be understood, from regional to global spatial scales, and from monthly to inter-annual time scales.
Artificial intelligence techniques, particularly in machine learning, will help us to estimate the degree of uncertainty in the observed data, and in the models’ predictions.
Project Aims and Methods
1) Collect observations. We have established a time-series of ocean surface CO2 measurements, made on board commercial vessels, between the UK and Caribbean, as part of an international effort to collect such high quality data, which is publically available through the Surface Ocean CO2 Atlas (SOCAT, 2018). Within this project we will continue the collection of these measurements, the data quality control and the contribution to the global dataset in SOCAT.
We’ll be using these in situ observations, together with data from Earth Observations and Reanalyses, in statistical machine learning and AI models to improve our understanding of the marine carbon cycle and the biogeochemical drivers of its variability, and the air-sea CO2 flux:
2) Develop dynamic marine biogeochemical regimes. Observations are, by their nature, sparse in time and space; mapping techniques are therefore required to produce spatially complete fields at regular time intervals. A number of ocean divisions have been used for this (including Longhurst, 2007; Landschützer et al., 2014; Fay and McKinley, 2014; Watson et al., 2009; Reygondeau et al., 2013), but these are either static in space and time, contain unrealistically straight boundaries, or are computationally expensive to produce. We’ll be using statistical machine learning and AI methods to develop a new observation-based, time-changing (dynamic) division of ocean biogeochemical regimes, that are biogeochemically realistic and do not require extensive computational power.
3) Improve air-sea CO2 flux estimates. This will enable us to produce improved regional to global air-sea CO2 fluxes, at monthly through inter-annual time scales. We will rigorously test the uncertainties of our flux estimates, and perform validations and comparisons with published results and model outputs (e.g. CMIP6 results; Eyring et al., 2016).
The supervisors are happy to discuss the project’s details to suit a candidate.
Due to the interdisciplinary nature of this project, a training plan will be developed according to the background and expertise of the successful candidate. This will include health & safety for laboratory and field work, applying best practices and standard operating procedure in obtaining high quality observations, effective communication for international, interdisciplinary research, AI techniques, statistical analyses, and high quality scientific writing.
Eyring et al. (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. GMD, 9, 1937-1958, doi:10.5194/gmd-9-1937-2016
Fay and McKinley (2014) Global open-ocean biomes: mean and temporal variability. ESSD, 6, 273-284, doi:10.5194/essd-6-273-2014
Intergovernmental Panel on Climate Change (2013) The Physical Science Basis. http://www.ipcc.ch/report/ar5/wg1/
Landschützer et al. (2014) Recent variability of the global ocean carbon sink. GBC, 28, 927-949, doi:10.1002/2014GB004853
Longhurst (2007) Ecological Geography of the Sea. Academic Press, London.
Le Quéré et al. (2017) Global Carbon Budget 2017. ESSD, 10, 405-448, doi.org/10.5194/essd-10-405-2018
Reygondeau et al. (2013) Dynamic biogeochemical provinces in the global ocean. GBC, 27, 1-13, doi:10.1002/gbc.20089
SOCAT (2018) https://www.socat.info/
Watson et al. (2009) Tracking the variable North Atlantic Sink for atmospheric CO2. Science, 326, 1391, doi:10.1126/science.1177394