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Postgraduate research project

Using machine learning to improve predictions of ocean carbon storage by marine life

Fully funded (UK and international)
Type of degree
Doctor of Philosophy
Entry requirements
2:1 honours degree View full entry requirements
Faculty graduate school
Faculty of Environmental and Life Sciences
Closing date

About the project

Carbon dioxide (CO2) dissolved in seawater is used by marine phytoplankton to grow, supporting the ocean’s foodweb and resulting in a downward flux of organic matter (the 'biological carbon pump' (BCP)) that sequesters carbon in the ocean and maintains atmospheric CO2 concentrations roughly 1/3 lower than in its absence. However, we lack an understanding of the relative importance of the BCP’s drivers and how they will respond to climate change, and, consequently, a robust way of predicting change in the BCP itself. Such change may decrease the ocean's role as a carbon reservoir, impacting national commitments to "net-zero". The recent IPCC Report (, Ch.5, section asserted “high confidence that feedbacks to climate will arise from alterations to the magnitude and efficiency of the BCP”, but that the attributed drivers differ significantly. A deeper understanding is needed of the BCP in our predictive models where there is no consensus on how it is represented. Machine learning (ML) techniques provide powerful tools to infer the underlying dominant causal influences on the BCP and how they shift into the future across a range of earth system models, allowing more robust assessment of global change in the BCP.

For full project details visit the Inspire project page.

Lead supervisor

  • Doctor Adrian Martin (National Oceanography Centre)


  • Professor Steph Henson (National Oceanography Centre)
  • Professor Zudi Lu (University of Southampton)
  • Doctor B.B. Cael (National Oceanography Centre)
  • Doctor Sian Henley (University of Edinburgh)