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.

冰架形成东、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（背力或支撑力），部分抵消了内陆冰流向海洋的驱动力1。自世纪中期以来，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，甚至灾难性地消失。这导致内陆冰流加速3高达8倍4，一些盆地在崩解后约20年仍在调整到崩塌前的速度5。加速内陆冰后，大陆架崩溃，导致海平面上升的显着贡献，从这个区域5，6，未来的贡献预计将在很大程度上取决于剩余的货架在半岛和其他地方的状态和命运在第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溃时间的现实预测。因此，南极冰架崩塌风险评估仍然是冰冻圈科学的重要目标。该项目将利用卫星和气候模型“大数据”构建冰架崩塌风险统计模型。尽管有许多卫星观测和代理重建以前的崩溃事件，冰架内发生的管理过程的复杂性，到目前为止，排除了使用物理为基础的预测模型。然而，新出现的和大量的观测“大数据”记录的冰架属性，和调查超过半个世纪的冰架崩溃episodes2，很好地结合在一个统计模型框架。贝叶斯非参数提供了一类以数据为主导的统计模型，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）观察来模拟一种现象，但足够灵活，可以在未来纳入新的数据集。这种质量对于模拟冰架崩塌风险至关重要，因为新的观测和信息经常可以获得。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，我们将寻求分配概率的主要崩溃事件在个别冰架在未来100年的过程中。然后，这些概率可以用于基于物理的冰盖模型，以改善南极冰盖对海平面上升的贡献的预测。