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Dr Stephen Chuter

Remote sensing of ice sheet dynamics and mass balance

My research focuses on using remote sensing satellite observations to better understand the response of the Antarctic and Greenland Ice Sheets to global climate change. These observations are critical to our understanding of how the ice sheets are responding to changes in both atmospheric and oceanic forcing over multiple decades; subsequently improving our ability to assess their present-day contribution to Global Mean Sea Level (GMSL) rise and help inform future projections. I combine these observations with novel statistical approaches to produce reconciled estimates of ice sheet mass balance over the last two decades. These estimates have been included in multi-method assessments of the global sea level budget (World Climate Research Programme). Additionally, I used these satellite observations to monitor ice sheet processes and their subsequent impact on ice dynamics.

My PhD used the novel European Space Agency (ESA) CryoSat-2 satellite radar altimetry mission to better quantify the thickness of the Antarctic ice shelves. These provide buttressing to the grounded ice sheet, regulating its flow and providing stability to the grounded ice sheet. In addition, this new thickness product was used to assess uncertainties in ice sheet mass balance at the drainage basin scale. My PhD was funded by a Natural Environment Research Council (NERC) studentship and received a commendation from the Faculty of Science. Additionally, it won the University of Bristol 2018 Faculty Doctoral Prize for the Natural Environment and Life Sciences. 

My current research combines a diverse range of satellite observations and ground based datasets in order to produce a reconciled estimate of ice sheet mass balance for both Greenland and Antarctica, in addition to quantifying the processes driving this change (e.g. changes in surface mass balance or changes in ice sheet dynamics/ice flow). Combining these observations is challenging due to their varying spatial and temporal resolutions in addition to them measuring a combination of multiple processes, which are challenging to separate without the use of forward geophysical models. To address this challenge, I use statistical approaches such as Bayesian Hierarchical Modelling (BHM) in order to integrate these observations. This research is part of the European Research Council (ERC) GlobalMass Horizon2020 project, based at the university of Bristol. 

Research keywords

  • Remote Sensing
  • Glaciology
  • Cryosphere
  • Sea level
  • Climate Change
  • Big Data Analytics