We implement a data assimilation framework for integrating ice surface and terminus position observations into a numerical ice-flow model. The model uses the well-known shallow shelf approximation (SSA) coupled to a level set method to capture ice motion and changes in the glacier geometry. The level set method explicitly tracks the evolving ice-atmosphere and ice-ocean boundaries for a marine outlet glacier. We use an Ensemble Transform Kalman Filter to assimilate observations of ice surface elevation and lateral ice extent by updating the level set function that describes the ice interface. Numerical experiments on an idealized marine-terminating glacier demonstrate the effectiveness of our data assimilation approach for tracking seasonal and multi-year glacier advance and retreat cycles. The model is also applied to simulate Helheim Glacier, a major tidewater-terminating glacier of the Greenland Ice Sheet that has experienced a recent history of rapid retreat. By assimilating observations from remotely-sensed surface elevation profiles we are able to more accurately track the migrating glacier terminus and glacier surface changes. These results support the use of data assimilation methodologies for obtaining more accurate predictions of short-term ice sheet dynamics.
翻译:我们实施数据同化框架,将冰表面和终点位置观测纳入一个数字冰流模型中。模型使用众所周知的浅架近似(SSA)加上一个定级方法来捕捉冰川几何中的冰运动和变化。定级方法明确跟踪海洋出口冰川的冰-大气和冰-海洋边界的变化。我们使用一个综合变形卡尔曼过滤器,通过更新描述冰界面的定级功能,将冰表面高地和横向冰层的观测同化。关于理想化海洋间距冰川的量化实验显示了我们数据同化方法在跟踪季节性和多年冰川推进和后退周期方面的有效性。模型还用于模拟海尔海姆冰川冰川冰川,这是格陵兰冰原的主要潮水-断层冰川,最近曾经历迅速退缩的历史。通过从遥感地表海拔图中进行同化观察,我们能够更准确地跟踪冰川迁移的冰川终点和冰川表面变化。这些结果支持数据同化方法的使用,以获得对短期冰层动态的更准确的预测。