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Cambridge NERC Doctoral Training Partnerships

Graduate Research Opportunities

Lead Supervisor: Andrew Meijers, BAS

Co-Supervisors: Peter Haynes, DAMTP, and Mark Webb, MetOffice Hadley Centre

Importance of the area of research concerned: 
The Southern Ocean is the principle region where the deep ocean, cryosphere and atmosphere may exchange properties with one another. This is the key pathway for heat, carbon and nutrients into the ocean interior and has a great impact on global climate. However, such exchanges of heat, freshwater and CO2 within a complex dynamical system creates 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. In turn the freshwater stratifies 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 climate models do not produce coherent future projections for the Southern Ocean, largely due to differences in how such dynamical systems are modelled. This is a major source of uncertainty for predictions of warming and sea level rise and needs to be addressed to improve climate forecasting.
Project summary : 
This project will utilise new data analysis techniques and algorithms to examine a suite of state-of-the-art climate models and identify and characterise the key dynamical relationships between ocean-atmosphere-ice variables that set the wide range of future polar climate projections currently causing uncertainty in 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 advances in machine learning and data science to the large and multi-dimensional datasets of climate models. This work will apply such techniques to the Southern Ocean in coupled climate models to discover the key parameters governing their response to both natural and human forcing, and in doing so understand the dynamics of the real polar climate system and reduce uncertainties in climate projections.
What will the student do?: 
Existing analysis of the Southern Ocean in climate models have found a wide range of future predictions for the ocean state, particularly regarding vertical stratification, heat content and horizontal circulations (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 machine learning algorithms such as dimensionality reduction, clustering and graphical networks (e.g. Ebert-Uphoff & Deng 2012) to the latest CMIP5 and CMIP6 ensembles of coupled climate models. Unguided learning techniques in particular offer great potential to uncover presently unknown or misunderstood relationships within models. The student will contrast the key state space parameters between models and work to associate them with future model states (e.g why some models convect while others don’t) and constrain existing projections using the dynamical knowledge gained. Ultimately the student may have the opportunity to test their predictions by running a regional model of the Southern ocean.
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, vol 25, pp. 5648-5665
You can find out about applying for this project on the British Antarctic Survey (BAS) page.