De-tuning Albedo parameters in a coupled climate ice sheet model to simulate the North American ice sheet at the last glacial maximum

• Published in: Journal of Geophysical Research: Earth Surface
• Main Authors: Gandy, N., Astfalck, L. C., Gregoire, L. J., Evanovich, R. F., Patterson, V. L., Sherriff-Tadano, S., Smith, R. S., Williamson, D., Rigby, R.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2023
• Subjects: Greenland; Ice Sheet
• Online Access: https://centaur.reading.ac.uk/112998/; https://centaur.reading.ac.uk/112998/1/De%E2%80%90Tuning%20Albedo%20Parameters%20in%20a%20Coupled%20Climate%20Ice%20Sheet%20Model%20to%20Simulate%20the%20North%20American%20Ice%20Sheet%20at%20the%20Last%20Glacial%20Maximum.pdf

The Last Glacial Maximum extent of the North American Ice Sheets is well constrained empirically but has proven to be challenging to simulate with coupled Climate-Ice Sheet models. Coupled Climate-Ice Sheet models are often too computationally expensive to sufficiently explore uncertainty in input parameters, and it is unlikely that values calibrated to reproduce modern ice sheets will reproduce the known extent of the ice at the Last Glacial Maximum. To address this, we run an ensemble with a coupled Climate-Ice Sheet model (FAMOUS-ice), simulating the final stages of growth of the last North American Ice Sheets' maximum extent. Using this large ensemble approach, we explore the influence of numerous uncertain ice sheet albedo, ice sheet dynamics, atmospheric, and oceanic parameters on the ice sheet extent. We find that ice sheet albedo parameters determine the majority of uncertainty when simulating the Last Glacial Maximum North American Ice Sheets. Importantly, different albedo parameters are needed to produce a good match to the Last Glacial Maximum North American Ice Sheets than have previously been used to model the contemporary Greenland Ice Sheet due to differences in cloud cover over ablation zones. Thus, calibrating coupled climate-ice sheet models on one ice sheet may produce strong biases when the model is applied to a new domain.