We consider the scientifically challenging and policy-relevant task of understanding the past and projecting the future dynamics of the Antarctic ice sheet. The Antarctic ice sheet has shown a highly nonlinear threshold response to past climate forcings. Triggering such a threshold response through anthropogenic greenhouse gas emissions would drive drastic and potentially fast sea level rise with important implications for coastal flood risks. Previous studies have combined information from ice sheet models and observations to calibrate model parameters. These studies have broken important new ground but have either adopted simple ice sheet models or have limited the number of parameters to allow for the use of more complex models. These limitations are largely due to the computational challenges posed by calibration as models become more computationally intensive or when the number of parameters increases. Here, we propose a method to alleviate this problem: a fast sequential Monte Carlo method that takes advantage of the massive parallelization afforded by modern high-performance computing systems. We use simulated examples to demonstrate how our sample-based approach provides accurate approximations to the posterior distributions of the calibrated parameters. The drastic reduction in computational times enables us to provide new insights into important scientific questions, for example, the impact of Pliocene era data and prior parameter information on sea level projections. These studies would be computationally prohibitive with other computational approaches for calibration such as Markov chain Monte Carlo or emulation-based methods. We also find considerable differences in the distributions of sea level projections when we account for a larger number of uncertain parameters. For example, based on the same ice sheet model and data set, the 99th percentile of the Antarctic ice sheet contribution to sea level rise in 2300 increases from 6.5 m to 13.1 m when we increase the number of calibrated parameters from three to 11. With previous calibration methods, it would be challenging to go beyond five parameters. This work provides an important next step toward improving the uncertainty quantification of complex, computationally intensive and decision-relevant models.

中文翻译：

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一种基于粒子的快速方法，用于校准南极冰盖的3-D模型

我们考虑了解过去并预测南极​​冰盖未来动态的科学挑战性工作和与政策相关的任务。南极冰盖对过去的气候强迫表现出高度非线性的阈值响应。通过人为温室气体排放触发这种阈值响应，将导致海平面急剧上升且可能快速上升，这对沿海洪灾风险具有重要意义。先前的研究结合了来自冰盖模型和观测的信息来校准模型参数。这些研究开辟了重要的新领域，但要么采用简单的冰盖模型，要么限制了参数的数量，以允许使用更复杂的模型。这些限制主要是由于模型变得计算量更大或参数数量增加时，校准带来的计算挑战。在这里，我们提出一种缓解此问题的方法：一种快速顺序蒙特卡洛方法，该方法利用了现代高性能计算系统提供的大规模并行化功能。我们使用模拟示例来说明我们基于样本的方法如何为校准参数的后验分布提供准确的近似值。计算时间的急剧减少使我们能够提供对重要科学问题的新见解，例如，上新世时代数据和先前参数信息对海平面投影的影响。这些研究与其他用于校准的计算方法（例如马尔可夫链蒙特卡洛法或基于仿真的方法）在计算上是相形见prohibit的。当我们考虑大量不确定参数时，我们还发现海平面投影的分布存在很大差异。例如，基于相同的冰盖模型和数据集，当将校准参数的数量从三个增加到11个时，南极冰盖对2300年海平面上升的贡献的第99个百分位数从6.5 m增加到13.1 m。以前的校准方法，超越五个参数将是一个挑战。这项工作为改进复杂，计算密集型和决策相关模型的不确定性量化提供了重要的下一步。