Multiproxy quantification of NE Irish Ice Sheet deglaciation timing and dynamics

爱尔兰东北部冰盖消冰时间和动态的多代理量化

• 批准号: 2548383
• 金额: --
• 依托单位: Royal Holloway University of London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2548383
• 关键词: Multiproxy; quantification; NE; Irish; Ice

The last British-Irish Ice Sheet (BIIS) is an excellent analogue for the vast ice masses currently occupying Antarctica and Greenland. Reconstructions of the BIIS therefore have the potential to inform us of future trajectories of large-scale ice mass loss. However, large gaps in our knowledge of major ice (re)advance and retreat in a rapidly shifting climate still hinder the efficacy of the BIIS as a comprehensive predictor of future deglaciation elsewhere in the world. To fill these gaps, the ambition of this project is to understand the role of climatic, sea-level, and internal drivers related to ice sheet stability and ice divide migration in the north-western sector of the BIIS. We will develop a brand new geomorphological understanding of little understood (re)advances of ice in Northern Ireland by employing unparalleled 0.4 m resolution digital elevation models. This will contextualise a new chronology for this area using radiocarbon and cosmogenic nuclide dating campaigns, and the first tephrostratigraphic framework for the Late Glacial in Ireland. This high-resolution multiproxy approach will allow us to date and directly compare rates of deglaciation across Ireland and Britain, providing new insights into the timing and drivers of ice-mass recession critical for improving BIIS models.

最后的英国-爱尔兰冰盖（BIIS）是目前占据南极洲和格陵兰岛的巨大冰块的绝佳类比。因此，BIIS 的重建有可能让我们了解未来大规模冰块损失的轨迹。然而，我们对快速变化的气候中主要冰（重新）前进和后退的了解存在巨大差距，仍然阻碍了 BIIS 作为世界其他地区未来冰消融综合预测的有效性。为了填补这些空白，该项目的目标是了解气候、海平面以及与 BIIS 西北部冰盖稳定性和冰分流迁移相关的内部驱动因素的作用。我们将通过采用无与伦比的 0.4 m 分辨率数字高程模型，对北爱尔兰鲜为人知的冰（重新）进展形成全新的地貌理解。这将利用放射性碳和宇宙成因核素测年活动，以及爱尔兰晚冰期的第一个地层学框架，为该地区建立一个新的年表。这种高分辨率的多代理方法将使我们能够确定并直接比较爱尔兰和英国的冰川消退速度，从而为冰体退缩的时间和驱动因素提供新的见解，这对于改进 BIIS 模型至关重要。