PROJECT TITLE: Untangling the web: Using machine learning to understand dynamical couplings between the Southern Ocean, cryosphere and atmosphere and how they impact our future climate

Grand Challenge Theme(s): Artificial Intelligence & the Data-Driven Economy Lead Institution: British Antarctic Survey Main Supervisor: Dr. Andrew Meijers, Polar Oceans, British Antarctic Survey Co-Supervisor: Dr. Emily Shuckburgh, Polar Oceans, British Antarctic Survey Co-Supervisor: Prof. Geoffrey Vallis, University of Exeter Co-Supervisor: Mark Webb, Met Office Hadley Centre Project Enquiries: Dr Andrew Meijers - [email protected]

Project Keywords

Southern Ocean, Climate, Machine Learning, Artificial Intelligence, Prediction

Project Background

The Southern Ocean surrounding Antarctica is the principal region where the deep ocean, cryosphere and atmosphere 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 within a complex dynamical system creates difficult-to-predict coupled feedbacks that may profoundly influence climate. Presently climate models, including the UK’s national model, do not produce coherent future projections for the Southern Ocean, representing a major source of uncertainty for global predictions (Meijers 2014).

Recently emerged machine learning (ML) techniques have a great, and largely untapped, potential to improve our analysis of climate models and our understanding of the wider climate system. This project seeks to apply these exciting new ‘climate informatics’ (Monteleoni et al. 2012) to the problem of understanding how polar change will impact global climate.

Project Aims and Methods

This project will utilise emerging AI tools to examine a suite of state-of-the-art climate models and characterise the key dynamical relationships setting the state of the polar climate. It will link these causal relationships and our understanding of the observed ocean with possible future states of the Southern Ocean and use the relationships uncovered to reduce our uncertainty in global climate projections.

The student will apply ML algorithms such as dimensionality reduction, clustering and graphical networks (Ebert-Uphoff & Deng 2012) to ensembles of coupled climate models. Unsupervised learning techniques offer considerable potential to reveal relationships within models. Challenges include adapting existing algorithms to deal with the very high-dimension data and its non-stationary, non-isotropic and highly- correlated nature. The student will identify the key parameters in each model and contrast relationships across models to understand the role of ocean-atmosphere-ice coupling in setting future model states. They will constrain projections using the knowledge uncovered combined with known present-day properties and dynamics to reduce uncertainty in future global climate projections. 1

The AI/ML skills the student will acquire, and tools they develop, could be applied to a wide range of “climate services” as well as problems beyond climate science in support of the data-driven economy.

Candidate

This project would suit a numerate student with a background in mathematics, physics, data science, machine learning or an equivalent quantitative field, and a desire to apply new techniques to climatic questions. This work is data driven so experience coding and dealing with large datasets will be an advantage.

Research collaborations and CASE award

The student will be primarily based at BAS Cambridge where Dr. Meijers leads the BAS Open Oceans team and there is an established set of researchers looking at all aspects of Southern Ocean climate. The student will be part of the growing ML group at BAS led by Dr Shuckburgh and also have access to collaborators in ML and AI at the University of Cambridge. Prof. Vallis at Exeter will provide his great knowledge of ocean- atmosphere physics and climate dynamics to assist in contextualizing relationships that emerge from the algorithms applied to the climate model data. This project has a CASE award with the Met Office where the student will spend at least 3 months during the PhD. Mark Webb at the Met Office will provide an interface between the student and the UK climate model and Met Office machine learning experts. Frequent email and teleconference contact will link the groups, and it is envisaged the student will also visit Exeter for several weeks each year to work directly with the teams there.

Training

The student will join a growing research group (currently three other Cambridge-based PhD students and a postdoc) applying ML to climate problems and will be able to attend graduate-level ML courses. They will be trained in a range of tools and techniques, including the processing and analysis of multivariate climate model output. Students will have access to training in unsupervised machine leaning techniques, Python and relevant machine learning libraries and in climate modelling/ocean dynamics. Students will have the opportunity to interface with the Met Office climate model development process and to feed into ongoing improvements to the UK-Earth System Model.

References / Background reading list

Meijers, A.J.S. “The Southern Ocean in the Coupled Model Intercomparison Project phase 5” Phil. Tran. R. Soc. A, (2014): 20130296 Monteleoni, C., et al. “Climate Informatics” in Computational Intelligent Data Analysis for Sustainable Development (2013): 81-126 Ebert-Uphoff, I. and Deng, Y. “Causal discovery for climate research using graphical models” Journal of Climate (25) (2012) 5648-5665

How to apply: Please send a full CV, statement of interest, copies of degree certificates and transcripts to: Dr Andrew Meijers [email protected] no later than 9th July 2018 1600 BST (1500 UTC).

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