NSFPLR-NERC: Processes, drivers, predictions: Modeling the response of Thwaites Glacier over the next century using ice/ocean coupled models

NSFPLR-NERC：过程、驱动因素、预测：使用冰/海洋耦合模型对思韦茨冰川在下个世纪的响应进行建模

• 批准号: NE/S006745/1
• 负责人: Hilmar Gudmundsson
• 金额: $ 89.6万
• 依托单位: Northumbria University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FS006745%2F1
• 关键词: NSFPLR; NERC; Processes; drivers; predictions; NSFPLR; NERC; Processes; drivers; predictions

We propose to conduct coupled ice-ocean numerical simulations of Thwaites Glacier (TG) to predict its future contribution to global sea level change, and to provide both statistical and structural error estimates using three state-of-the-art ice flow numerical ice-flow models (ISSM, Úa and STREAMICE) coupled with the ocean model MITgcm. We will implement and improve the representation of several key physical processes (calving, ice damage, mechanical basal conditions) that have either not been included, or poorly represented in previous ice-flow modelling work. We will quantify the relative role of different proposed external drivers of change (e.g., ocean-induced ice-shelf thinning, loss of ice-shelf pinning points), and explore systematically the stability regime of TG with the aim of identifying internal thresholds separating stable and unstable grounding line retreats. Using inverse methodology, we will produce new physically consistent high-resolution (300 m) data sets on ice-thicknesses from available radar measurements. Furthermore, we will generate several new remote sensing data sets on ice velocities and rates of elevation change. These will be used to constrain and validate our numerical models, and will also be highly valuable as stand-alone data sets.

我们将实施并改善未包括的几个关键物理过程（产犊，冰损伤，机械基本条件）的代表，或者在先前的冰流建模工作中代表不当。我们将量化不同提议的外部变化驱动因素的相对作用（例如，海洋引起的冰架稀疏，冰架钉钉损失），并系统地探索TG的稳定性状态，目的是识别内部阈值，以识别稳定且不稳定的接地线撤退的内部阈值。使用逆方法，我们将对可用雷达测量结果产生有关冰厚度的新物理一致的高分辨率（300 m）数据集。此外，我们将生成有关冰速度和高程变化速率的几个新的遥感数据集。这些将用于限制和验证我们的数值模型，并且作为独立数据集也将非常有价值。