"Using Complex Networks to Advance our Understanding of the Polar Climate System"
PhD project title:
Using Complex Networks to Advance our Understanding of the Polar Climate System
BSc: Geology with Geophysics, University of Leicester
MSc: Petroleum Geophysics, Imperial College London
Will is a NERC DTP PhD student within the Polar Climate Group at UCL. He has a background in Geophysics, in particular sub-surface imaging & modelling. His 3 years within the energy sector exposed him to various aspects of time-series analysis, data processing and tomographic inversion methods, with regards to seismic data. Will has a strong interest in data analysis and hopes to improve his knowledge of statistical forecasting and machine learning with big data during the course of his PhD.
Arctic sea ice is a thin blanket of ice which covers an area approximately equal to the size of Europe. Climate change has been shown to exhibit direct and exacerbated environmental and societal consequences for the Arctic, which in turn has implications for the global climate. Sub-seasonal and annual variability of various Arctic climatic components (cloud cover, river runoff, ocean fluxes or atmospheric circulation), and the interactions between them is little well understood, hence making predictability over similar time periods difficult. Complex Networks aims to exploit the predictive power associated with spatio-temporal relations between various Arctic climate components to provide improved understanding on the higher-order structure of statistical interrelationships.
The aims of this project are to (1) construct a Complex Network Framework from which statistical association between various Arctic climate components will be derived from satellite time series data. Secondly, (2) information gained from the Complex Network Analysis will be fed into a machine learning regression model, to improve Arctic sea ice predictability on a sub-seasonal to annual time scale. Finally, (3) the new insights into the interrelations between Arctic teleconnections will aim to provide improved understanding of the predictive abilities of coupled climate models.