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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

基本信息

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

项目摘要

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.

我们建议进行耦合的冰-海洋数值模拟的Thwaites冰川（TG），以预测其未来的全球海平面变化的贡献，并提供统计和结构误差估计使用三个国家的最先进的冰流数值冰流模型（ISSM，ESTA和STREAMICE）加上海洋模型MITgcm。我们将实施和改进几个关键的物理过程（产犊，冰的损害，机械基础条件），要么没有被包括在内，或在以前的冰流建模工作表现不佳的表示。我们将量化不同的拟议外部变革驱动因素的相对作用（例如，海洋引起的冰架变薄，冰架钉扎点的损失），并系统地探索TG的稳定性制度，目的是确定内部阈值分离稳定和不稳定的接地线撤退。使用逆方法，我们将产生新的物理上一致的高分辨率（300米）数据集的冰厚度从现有的雷达测量。此外，我们将生成几个新的遥感数据集的冰速度和海拔变化率。这些将用于约束和验证我们的数值模型，并且作为独立的数据集也将非常有价值。