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.

冰架构成了东部，西部和南极半岛冰片的浮动延伸，并在调节冰盖流量和全球海平面上升方面发挥了至关重要的作用。冰架的存在提供了抵抗力（背部或支撑）力，部分补偿了流向SEA1的内陆冰的驱动力。自20世纪中叶和卫星“大数据”观察时代以来，南极半岛的几个冰架已经大大撤退甚至灾难性地崩溃了2。这导致内陆冰流3的加速度高达84倍，其中一些盆地仍调整为崩解后约二十年的爆发前速度。货架倒塌后内陆冰的加速已导致从该地区的海平面上升为5,6的海平面上升，未来的贡献预计将很大程度上取决于半岛和南极洲其他地方的其余货架的状态和命运。对未来海平面上升的预测需要冰盖模型，并结合了对未来冰架倒塌时间的现实预测。因此，南极冰架崩溃的风险估计仍然是冰圈科学的重要目标。该项目将利用卫星和气候模型“大数据”来构建冰架倒塌风险的统计模型。尽管对先前崩溃发作的卫星观察和代理重建，但到目前为止，冰架内发生的处理过程的复杂性排除了基于物理的预测模型的使用。但是，冰架特性的新兴和实质性观察性的“大数据”记录以及半个世纪以上的冰架崩溃发作2在统计模型框架内很好地结合在一起。贝叶斯非参数提供了一类以数据为主导的统计模型，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）观察结果来建模现象，但具有足够的灵活性，可以使未来的新数据集包含在内。这种质量对于建模冰架崩溃的风险至关重要，在这里通常可以提供新的观察和信息。该项目将利用冰架的物理特性，环境条件和崩溃的时机历史，以估计未来崩溃事件的风险。特别是，在接下来的100年中，我们将寻求将概率分配给各个冰架的重大崩溃事件。然后，这些概率可以用于基于物理的冰盖模型中，以改善南极冰盖对海平面上升的贡献的预测。