Machine learning tools for pattern recognition in polar climate science

Profile Display Name:

Will Gregory

E-mail Address:

Start Year

2017 (Cohort 4)

Research interests:

Machine learning methods; Gaussian Processes, Complex Networks, Bayesian Inference, with applications in sea ice and polar climate.

Hobbies and interests:
PhD Project
PhD Title

Machine learning tools for pattern recognition in polar climate science

Research Theme

Earth, Atmosphere and Ocean Processes

Primary Supervisor
Primary Institution


Secondary Supervisor
Secondary Institution



Arctic sea ice is a thin blanket of ice (typically 2 – 3 metres thick) that covers an area roughly the size of Europe. The ice responds rapidly to changes in oceanic and atmospheric forcing, shrinking every summer to less than half its winter maximum extent. Because of these characteristics, sea ice can be seen as a “barometer” for measuring changes that occur in the atmosphere and the ocean, as well as in various other components of the Arctic climate system.

Pattern Recognition is a field dedicated to the development of generalised models which are able to identify regularities within a given data set. In this thesis, two novel techniques will be explored for exploiting such regularities within climate time series data; techniques which encompass aspects of both supervised and unsupervised machine learning and have been used extensively in a number of fields outside of climate science. These methods include the Bayesian inference approach of Gaussian Process Regression, and also Complex Networks. Through the application of these techniques, both independently and in conjunction, the aim is to improve our understanding of the polar climate system by exploiting patterns of sea ice variability and evaluating how they manifest within observational and model products, through seasonal sea ice forecasting, merging of satellite altimetry products, and an assessment of sea ice varibility with the next generation of coupled climate models from CMIP6.

Policy Impact
Background Reading
  • Pattern Recognition and Machine Learning (Bishop, 2006)
  • Gaussian Processes for Machine Learning (Rasmussen and Williams, 2006)
  • Spatio-temporal network analysis for studying climate patterns (Fountalis et al, 2014)
  • Publications
    Conferences and Workshops
    Social Links
    University Departmental Website:
    Personal Website:




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