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

冰架是东、西和南极半岛冰盖的浮动延伸，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（后退或支撑）力，部分补偿了内陆冰流向海洋的驱动力。自20世纪中期以来，在卫星“大数据”观测时代，南极半岛的几个冰架已经大幅退缩，甚至发生了灾难性的崩塌。这导致内陆冰流加速了多达84倍，有些盆地在崩解大约20年后仍在调整到崩解前的速度。大陆架崩塌后，内陆冰的加速融化对该地区的海平面上升起到了重要作用，预计未来的作用将在很大程度上取决于该半岛和南极洲其他地方剩余大陆架的状态和命运。对未来海平面上升的预测需要结合对未来冰架崩塌时间的现实预测的冰盖模型。因此，南极冰架崩塌的风险估计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先前崩塌事件的代理重建，但到目前为止，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，关于冰架特性的新出现的大量观测“大数据”记录，以及对半个多世纪以来冰架崩塌事件的调查，使它们能够很好地结合在一个统计模型框架内。贝叶斯非参数提供了一类以数据为导向的统计模型，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）观察结果来模拟一种现象，但它足够灵活，可以允许将来包含新的数据集。这一特性对于模拟冰架崩塌风险至关重要，因为在这种情况下，经常可以获得新的观测和信息。该项目将利用冰架的物理特性、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，我们将设法确定未来100年内个别冰架发生重大崩塌事件的概率。然后，这些可能性可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。

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.