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C423: Untangling the web: Using machine learning to understand dynamical couplings in the southern ocean, ice and atmosphere and how they impact our future climate (Lead Supervisor: Andrew Meijers, British Antarctic Survey)

Supervisors: Andrew Meijers (British Antarctic Survey), Peter Haynes (DAMTP) and Scott Hosking (British Antarctic Survey)

Importance of the area of research:

The Southern Ocean surrounding Antarctica is the principle region where the deep ocean, cryosphere and atmosphere may freely exchange properties with one another.   This is the major pathway for heat, carbon and nutrients into the ocean interior and has a disproportionately large impact on global climate.  However, such exchanges of active tracers such as heat, freshwater and CO2 within a complex dynamical system presents considerable potential for difficult-to-predict coupled feedbacks that may profoundly influence both regional and global climates.  For example the vertical upwelling of warm water from the deep ocean may be increased due to anthropogenically driven wind changes.  This brings more warm water to the surface, melting sea ice and adding freshwater to the system.  In turn the freshwater drives stronger stratification of surface waters and may reduce subsequent downwelling of heat and carbon enriched waters, reducing the ocean carbon/heat sink and feeding back atmospheric CO2, warming and wind increases. Presently IPCC climate models do not produce coherent future projections for the Southern Ocean, largely due to differences in how such dynamical systems are modelled.

Project summary:

This project will utilise emerging data analysis techniques and algorithms to examine the IPCC suite of state-of-the-art climate models and identify and characterise the key dynamical relationships between southern ocean-atmosphere-ice variables that set the wide range of future polar climate projections currently introducing uncertainty into future climate projections.  The field of ‘climate informatics' is an exciting and newly emerging one (Monteleoni et al. 2012), with great untapped potential for applying recent advances in machine learning and data science to the large and multi-dimensional datasets that climate data and models represent.  This work will apply such techniques to the Southern Ocean climate system in coupled climate models to discover the key parameters governing the response of the region to both natural and anthropogenic forcing.

What the student will do:

Present analysis of the Southern Ocean in coupled climate models has characterised the representation of the models vs observations and demonstrated the wide range of future predictions made (e.g. Meijers 2014).   However, there has been less effort in characterising the differences in dynamical relationships between models and uncovering how these may contribute to uncertainty in key parameters such as sea ice extent or ocean temperature.   The student will apply novel data analysis techniques and machine learning algorithms such as graphical networks (e.g. Ebert-Uphoff & Deng 2012) to the latest CMIP5/6 ensembles of coupled climate models.  Unguided learning techniques in particular offer considerable potential to uncover unknown or misunderstood relationships within models.  Challenges will include adapting existing algorithms to deal with the very high dimensional nature of the data and its non-stationary, non-isotropic, highly-correlated nature.  The student will contrast the state space parameters between models and work to associate such parameters with future model states and constrain present projections using the dynamical knowledge uncovered.

Please contact the lead supervisor directly for further information relating to what the successful applicant will be expected to do, training to be provided, and any specific educational background requirements.


Monteleoni, C., et al. 2013. Climate Informatics in Computational Intelligent Data Analysis for Sustainable Development: pp. 81-126

Meijers, A.J.S. 2014.  The Southern Ocean in the coupled model intercomparison project phase 5. Phil. Tran. R. Soc. A: 20130296

Ebert-Uphoff, I. and Deng, Y. 2012.  Causal discorver for climate research using graphical models. Journal of Climate (25) (2012) pp: 5648-5665

Follow this link to find out about applying for this project.

Other projects available from the Lead Supervisor can be viewed here.

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