Climate-ice sheet interactions during abrupt warming in the last deglaciation

Project Description

Climate-ice sheet interactions during abrupt warming in the last deglaciation
Supervisory research team: Brooke Snoll, Lauren Gregoire, Ruza Ivanovic, Climate-Ice research group

In this project, you will work with an interdisciplinary team of researchers, analysing climate-ice sheet model simulations to unlock the mysteries of past abrupt climate and sea level changes.

The last deglaciation (beginning ~21 thousand years ago) is period consisting of large-scale ice sheet melt, rising summer solar insolation, increasing greenhouse gas concentrations, and warming surface air temperatures. This relatively smooth transition from the Last Glacial Maximum (when ice sheets were at their largest extent) is also accompanied by well-documented abrupt events during which the climate warmed by 10 degrees in Greenland and sea level rose by 15 m within three centuries. Similar abrupt climate events are also observed further back in time, during the last 100 thousand years, called Dansgaard-Oeschger events. However, despite the ability to identify these events in temperature proxy records, precisely how they occurred, what triggered them, and if they are linked is still debated. This is because instabilities and complex interactions between the atmosphere, ocean, and ice sheets are at play during these abrupt events.

Within the Climate-Ice research group, we run complex climate and ice sheet models on high performance computers to understand these abrupt climate and sea level changes. We have successfully simulated spontaneous abrupt climate changes with the HadCM3 general circulation model revealing the complex climate instabilities responsible for past abrupt events. We have also applied the coupled climate-ice sheet model FAMOUS-ice to simulate glacial climate matching records of past climate and ice sheet changes. In new climate and ice sheet model simulations, we investigate the impact of abrupt climate change, such as abrupt B├Şlling/Aller├Şd warming (14.5 thousand years ago) on the Northern Hemisphere ice sheets to better understand the climate-ice sheet interactions during abrupt events.
As part of this work, you will compare the model output to proxy reconstructions of ice sheet extent and flow to evaluate model performance. Understanding how our models perform in comparison to observational data is an important stepping stone to simulating the climate more accurately and developing crucial understanding of past and future climate change.

During this 6-week research project you will:
– Be part of the collegiate and inclusive Climate-Ice research group which currently comprises two academics, three postgraduate researchers and three postdoctoral researchers working in climate science, glaciology, and statistics.
– Attend fortnightly Climate-Ice research group meetings, meet with your supervisory team weekly and present your work at the Climate-Ice group meeting at the end of your placement.
– Work with talented researchers to contribute to new and impactful research on abrupt climate and sea level change.
– Adapt existing Python code to calculate and visualize the similarities and differences between model output and proxy reconstructions of past ice sheet changes to evaluate model performance.
– Shadow researchers running global climate and ice sheet models on high performance computers and observe the day-to-day life of PhD students, postdoctoral researchers, and academics in the group.

– A-level mathematics or physics or equivalent
– Experience with a programming language, preferably Python
– Preferable but not required: background in climate science or quaternary environmental change or equivalent

Supervisor & Contact

Brooke Snoll,

How to Apply

  1. Complete the online REP application form, one for each project of interest, including a copy of your CV.
  2. Complete the EDI form (only one is needed, you do not need to submit more than one if you apply for multiple projects).  Although this is optional, if places are over-subscribed, preference will be given to under-represented groups.