Estimating the risk of Antarctic ice shelf collapse using Bayesian nonparametric statistical modelling.

使用贝叶斯非参数统计模型估计南极冰架崩塌的风险。

• 批准号: 2220335
• 金额: --
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2220335
• 关键词: Estimating; risk; Antarctic; ice; shelf

Ice shelves form the floating extensions of the East, West and Antarctic Peninsula ice sheets and play a crucial role in regulating ice sheet flow and global sea-level rise. The presence of an ice shelf provides resistive (back or buttressing) forces, which partly compensate the driving forces of inland ice flowing to the sea1. Since the mid-20th century and during the satellite 'big data' observational era, several ice shelves in the Antarctic Peninsula have substantially retreated or even catastrophically collapsed2. This has resulted in acceleration of inland ice flow3 by a factor of up to eight4, with some basins still adjusting to pre-collapse velocities some twenty years after disintegration5. Acceleration of inland ice following shelf collapse has resulted in significant contributions to sea-level rise from this region5,6, with future contributions expected to be heavily dependent on the state and fate of the remaining shelves in the peninsula and elsewhere in Antarctica7. Forecasts of future sea-level rise require ice sheet models incorporating realistic predictions of the timing of future ice shelf collapses. Risk estimation of Antarctic ice shelf collapse thus remains a important goal of the cryospheric sciences.This project will utilise satellite and climate model 'big data' to construct a statistical model of ice shelf collapse risk. Despite many satellite observations and proxy reconstructions of previous collapse episodes, the complexity of governing processes occuring within ice shelves so far precludes the use of physically-based forecast models. However, the emerging and substantial observational 'big data' record of ice shelf properties, and surveys of more than half a century of ice shelf collapse episodes2, lend themselves well to combination within a statistical model framework. Bayesian nonparametrics provide a class of data-led statistical models that adapt their complexity to the data itself. This approach incorporates existing (perhaps imperfect or incomplete) observations to model a phenomenon, yet is flexible enough to allow future inclusion of new datasets. This quality is essential to modelling ice shelf collapse risk, where new observation and information are often made available. The project will make use of ice shelf physical properties, environmental conditions and collapse timing histories to estimate the risk of future collapse events. In particular, we will seek to assign probabilities to major collapse events at individual ice shelves over the course of the next 100 years. These probabilities can then be used in physically-based ice sheet models to improve forecasts of the Antarctic ice sheet contribution to sea-level rise.

冰架是东、西、南极半岛冰盖的浮动延伸部分，在调节冰盖流动和全球海平面上升方面发挥着至关重要的作用。冰架的存在提供了阻力（反力或支撑力），这部分补偿了内陆冰流向海洋的驱动力1。 20世纪中叶以来，卫星“大数据”观测时代，南极半岛多个冰架大幅后退，甚至发生灾难性崩塌2。这导致内陆冰流3加速了高达八倍4，一些盆地在解体后大约二十年仍在调整至崩塌前的速度5。陆架崩塌后的内陆冰加速导致该地区海平面上升5,6，未来的贡献预计将在很大程度上取决于半岛和南极洲其他地区剩余陆架的状况和命运7。对未来海平面上升的预测需要冰盖模型结合对未来冰架崩塌时间的现实预测。因此，南极冰架崩塌的风险评估仍然是冰冻圈科学的一个重要目标。该项目将利用卫星和气候模型“大数据”构建冰架崩塌风险的统计模型。尽管有许多卫星观测和对先前崩塌事件的代理重建，但迄今为止冰架内发生的控制过程的复杂性阻碍了基于物理的预测模型的使用。然而，冰架特性的新兴大量观测“大数据”记录以及对半个多世纪冰架崩塌事件的调查2，很适合在统计模型框架内进行组合。贝叶斯非参数提供了一类以数据为主导的统计模型，可以使其复杂性适应数据本身。这种方法结合了现有的（可能不完美或不完整的）观察来对现象进行建模，但又足够灵活，可以允许将来包含新的数据集。这种质量对于冰架崩塌风险建模至关重要，因为通常可以提供新的观测结果和信息。该项目将利用冰架物理特性、环境条件和崩塌时间历史来估计未来崩塌事件的风险。特别是，我们将寻求对未来 100 年内各个冰架发生重大崩塌事件的概率进行分配。然后，这些概率可以用于基于物理的冰盖模型，以改进对南极冰盖对海平面上升贡献的预测。