Advances in Earth Science
), Xu ZHANG
), Jinbo ZAN
1, Xiaomin FANG
Yuao ZHANG, Xu ZHANG, Jinbo ZAN, Xiaomin FANG. Investigating Uncertainty of Simulating Northern Hemisphere Ice Sheet Evolution by Glacial Index Method[J]. Advances in Earth Science, 2023, 38(6): 619-630.
The Intergovernmental Panel on Climate Change Sixth Assessment Report (AR6) stresses threat of the continuous melting of polar ice sheets and hence rising global sea levels on our socioeconomic and living environment. However, large uncertainty remains in future projections of Earth’s ice sheet, which might be reduced by improving our understanding of its evolution history and associated dynamics by ice-sheet modeling. Glacial index method is an effective approach to investigate transient ice sheet change by interpolating discrete climate forcing into continuous climate forcing based on paleoclimate proxies. This indicates the choice of paleoclimate proxy might be of crucial impact on simulated transient ice sheet change. Here we investigate this issue with a focus on the tempo-spatial evolution of the Northern Hemisphere ice sheet during the last glacial cycle using two sets (six in total) of proxies representing global sea level and temperature changes, respectively. Three key conclusions are summarized in the following. First, the characteristics of proxy trajectory have a significant influence on the simulated ice volume’s evolutionary characteristics. Second, the presence of millennial-scale abrupt climate change events in proxies lowers the simulated overall ice volume when tendency and amplitude of proxies are similar. Third, ice sheet extent is constrained by the summer 0 °C isotherm which is modulated by the tendency and amplitude of different proxies, even when subjected to the same Last Glacial Maximum climate forcing. Therefore, our results emphasize the need to carefully consider the characteristics of paleoclimate proxies when using the glacial index method for studying global ice sheet changes over time. Understanding the limitations and potential biases associated with the chosen proxies is crucial to avoid misinterpretation and overstatement of modeling results.