Global climate change is predicted to alter the ocean’s biological productivity with implications for fisheries and climate. Long-term changes in phytoplankton abundance projected by ocean coupled physical-biogeochemical models predict declining globally averaged phytoplankton abundance, associated carbon fluxes, and widespread deoxygenation (Kwiatkowski et al., 2020). However, observational data that could be used to verify current climate trends is limited in both space and time, particularly below the ocean surface. The advent of the bgc-Argo programme (https://biogeochemical-argo.org/) has vastly increased the quantity of biogeochemical data available for analysis (currently > 1500 floats have been deployed since 2002). The data are, however, scattered in space and time, making it difficult to deduce either the global mean 3-D distribution of biogeochemical properties, or how they may change seasonally or from year to year. This results in some fundamental gaps in our knowledge of contemporary ocean biogeochemistry, and the potential future response to climate change. For example, what is the global distribution of the subsurface chlorophyll maximum? How does the efficiency with which particulate organic carbon is stored in the ocean vary globally (e.g. Dall’Olmo and Mork, 2014)? This project aims to exploit the full potential of the bgc-Argo network to answer these questions by using space-time modelling (Hammond et al., 2017) to infer global spatial and temporal variability in critical biogeochemical processes.
All bgc-Argo data is freely available for download. The project will focus initially on profiles of particle backscatter (as a proxy for particulate organic carbon) and chlorophyll concentration. The student will adapt current state-of-the art Bayesian spatio-temporal modelling methodology using R packages spTimer and bmstdr (Bayesian modelling of Spatio-Temporal Data with R) recently developed by supervisor Sahu. This new user-friendly package is very well suited for this application as it incorporates automated model fitting and validation using a range of data sources, including those where data are irregularly (and sparsely) observed in space and time. For example, bmstdr can be used to produce an annual map of ocean temperature (see below) by using very irregularly observed Argo float data (profiles locations are shown as dots in the map).
This project will adapt the bmstdr package to model long term spatio-temporal variability in proxies for marine productivity and carbon fluxes. Where necessary, and depending on the skill level of the student, the project will also extend and develop bmstdr further to meet the challenges of the new application. For example, we envision a multivariate extension to jointly model correlated data such as ocean temperature and salinity.
The INSPIRE DTP programme provides comprehensive personal and professional development training alongside extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial/policy partners. The student will be registered at the University of Southampton and hosted at the National Oceanography Centre (Southampton). Specific training will include (both supervisor-led, and through training courses): space-time modelling, Bayesian analysis, relevant aspects of ocean biogeochemistry and its interaction with climate (e.g. IMBER ClimEco summer school). The student will be part of the Ocean Biogeochemistry and Ecosystems group at NOC, which is renowned globally as one of the leading centres of excellence in biological research with plankton ecologists, numerical modellers, remote sensing specialists, theoreticians and geochemists working together to address the most significant problems in biogeochemical oceanography. The student will receive training in methods of research by the NOC Graduate School and INSPIRE programme, and may attend appropriate university Masters level lectures to gain relevant background knowledge (e.g. modules on Global Ocean Carbon Cycle and Computational Data Analysis). Presentation of the results at national and international conferences will be expected and encouraged. There will also be the opportunity to participate in a research cruise.
Dall'Olmo, G., and Mork, K. A. (2014), Carbon export by small particles in the Norwegian Sea, Geophys. Res. Lett., 41, 2921– 2927, doi:10.1002/2014GL059244.
Hammond, M. L., C. Beaulieu, S. K. Sahu, and S. Henson (2017), Assessing trends and uncertainties in satellite-era ocean chlorophyll using space-time modeling, Global Biogeochemical Cycles, 31(7), 1103-1117, doi: 10.1002/2016GB005600
Kwiatkowski, L., Torres, O., Bopp, L., Aumont, O., Chamberlain, M., Christian, J. R., Dunne, J. P., Gehlen, M., Ilyina, T., John, J. G., Lenton, A., Li, H., Lovenduski, N. S., Orr, J. C., Palmieri, J., Santana-Falcón, Y., Schwinger, J., Séférian, R., Stock, C. A., Tagliabue, A., Takano, Y., Tjiputra, J., Toyama, K., Tsujino, H., Watanabe, M., Yamamoto, A., Yool, A., and Ziehn, T. (2020), Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections, Biogeosciences, 17, 3439–3470, doi: 10.5194/bg-17-3439-2020