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Overview

Dr Guy Paxman

Assistant Professor (Research)


Affiliations
AffiliationTelephone
Assistant Professor (Research) in the Department of Geography+44 (0) 191 33 41925

Biography

  • 2022 – Present: Research Fellow, Durham University, UK
  • 2019 – 2022: Postdoctoral Research Scientist, Lamont-Doherty Earth Observatory, New York, USA
  • 2015 – 2019: Ph.D., Durham University, UK
  • 2011 – 2015: MEarthSci - Earth Sciences, University of Oxford, UK
Research Groups
  • Sea Level, Ice and Climate
Research Overview

I am a polar geophysicist and geomorphologist with a particular interest in the long-term evolution of Earth's ice sheets and their sensitivity to climate change. My research focuses on the interactions between solid Earth processes, topography, and ice sheet dynamics, particularly Greenland and Antarctica. This involves  analysis of large geophysical and geological datasets, including radar-derived ice thickness and bed topography, gravity and magnetic anomalies, crustal and lithospheric properties, offshore sediment records, and satellite remote sensing, alongside numerical modelling and machine learning techniques.

The focus of my Ph.D. was the reconstruction of palaeotopography in Antarctica over multi-million year time scales, and the impacts of landscape evolution on ice-sheet behaviour and stability over the course of Antarctica's glacial history. Since then, I have worked on a US National Science Foundation (NSF) funded project to predict coastal responses to a changing Greenland Ice Sheet. This included constraining past ice extent and behaviour from geomorphological analysis of subglacial landscapes in the Greenlandic interior, improving models of solid Earth deformation across a range of timescales (i.e., elastic and viscous responses to ice sheet (un)loading), and developing projections of relative sea level and bathymetric change around the Greenland coastline in response to future warming scenarios.

In my current research at Durham as a Leverhulme Early Career Research Fellow I am developing automated techniques to map and classify subglacial geomorphological features in Antarctica and Greenland. I have also used supervised machine-learning routines such as random forest to quantify and intepret spatial patterns within large and hitherto under-utilised airborne geophysical datasets (e.g., radar, gravity, and magnetics) and help better understand the nature of the subglacial environment. The overarching aim of this project is to link landscape features to the process(es) responsible for their formation, and in turn better understand ice-sheet extent and behaviour through time.

Publications

Journal Article

Other (Digital/Visual Media)

Supervision students