Will Greenland Reach a Tipping Point? Using Ice Age Evidence to Predict Its Future

About the project

About the Project Background The Greenland Ice Sheet is the largest contributor to sea-level rise and may undergo irreversible retreat if critical warming thresholds are exceeded. Continued ice loss would raise sea levels, increasing the risk of coastal flooding, erosion, infrastructure damage, and population displacement worldwide. The pace and magnitude of these changes remain highly uncertain, especially beyond 2100. The risk of Greenland Ice Sheet collapse and the potential irreversibility of retreat are poorly understood. To address this major challenge, scientists combine climate and ice-sheet models with modern observations and geological evidence. Past climate records from the last Ice Age and warmer periods provide powerful tests of model reliability by revealing how ice sheets responded to very different conditions. Improving our ability to simulate past and present Greenland is essential to reduce uncertainty in future sea-level projections and assess long-term risks from climate and sea-level changes. PhD project This project uses FAMOUS-ice, a fast yet complex coupled climate-ice sheet model combining a general circulation atmosphere-ocean model with a dynamic ice-sheet model. Its speed and complexity make it ideal for quantifying the uncertainty in future Greenland stability and for studying the how geological evidence can improve confidence in sea level projections. Previous work showed FAMOUS-ice can reproduce the Last Glacial Maximum (21,000 years ago when large ice sheets covered Canada and Northern Europe) well compared to detailed evidence of past ice sheet extent and flow and past climate. However, no parameter set simulates both the glacial period and modern Greenland well, indicating deficiencies in climate simulation or surface mass balance processes that are likely due to insufficient resolution in the atmosphere. The student will improve the model by doubling the atmospheric resolution and applying new downscaling methods for precipitation over ice sheets, moving from the coarse atmosphere component (50-100 km) to the higher resolution ice-sheet model (1-10 km) using Machine Learning and/or process-based methods. The updated model will be used to run large ensembles of simulations of past, present and future covering uncertain parameters and initial conditions. By comparing these simulations with modern Greenland observations and the last deglaciation of the Northern Hemisphere (including new data from western Canada), the student will rule out simulations inconsistent with data. The remaining subset of model configurations will be used to calculate a probabilistic distribution of Greenland’s long-term evolution, stability thresholds, irreversible ice-sheet retreat, and implications for societal risks associated with sea level rise. Applicant Profile The project would suit students from Physics, Mathematics, Oceanography, Meteorology, Climate Sciences, Natural Sciences, Earth/Environmental/Geographical Sciences. Experience in computer programming (e.g., Python, Fortran, C++, MATLAB, R…) or numerical modelling is highly desirable.

Research areas