CAREER: Accelerating Probabilistic Predictions of Sea-level Rise with Deep Learning

最近在预测地球冰川变化方面出现了一种新的模式，强调考虑冰物理和未来气候方面的未知因素，以便构建一系列合理的未来。在为海平面上升、生态变化和其他气候反馈做好准备时，这种基于概率的界限是必不可少的，在这些反馈中，了解最佳和最坏情况具有实际重要性。这项任务在计算上是困难的：冰川模型是昂贵的，特别是当需要运行数千次模拟来完全描述可能的结果时，可能需要数月或数年的时间。然而，理解这种分布对于迎接建设可持续未来的巨大挑战至关重要。该项目将使用深度学习来构建替代模型，替代接近原始模型的冰盖模型组件，但计算成本要低得多。此外，该项目将召集一所暑期学校，并开发教育模式，以整合工作的研究和教育组成部分。特别是，该项目将使用几何和生成式深度学习与高性能计算中的经典技术的新应用相结合，建立一个比传统冰盖模型快数百倍的近似（或替代）模型。这样的加速反过来将使研究人员能够探索前所未有的未来情景，并迎接冰建模的挑战，以了解未来的全球影响。此外，为期九天的暑期学校将汇集来自世界各地的教师和学生，学习和分享将深度学习应用于冰川学问题的有效方法。为了帮助高中生学习了解气候变化及其不确定性所需的技能，并使用计算机解决相关的科学、经济和政策挑战，我们将开发针对高中生的教育模块，探索编程、数据和全球变化的交集。该项目由地球科学理事会共同资助，旨在支持人工智能/机器学习在地球科学领域的发展。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

A new paradigm has recently emerged in predicting changes to Earth’s glaciers that emphasizes accounting for unknowns with respect to ice physics and future climate in order to construct a range of plausible futures. Such probability-based bounds are essential in preparing for sea level rise, ecological changes, and other climate feedbacks in which understanding both best- and worst-case scenarios is of practical importance. This task is computationally difficult: glacier models are expensive, particularly when it can take months or years to run the many thousands of simulations required to completely characterize possible outcomes. However, understanding this distribution is essential in meeting the grand challenge of building a sustainable future. This project will use deep learning to construct surrogate models, replacements for ice sheet model components that approximate the original model, but which are much less computationally costly. In addition, this project will convene a summer school and develop educational models to integrate the research and educational components of the work. In particular, this project will use geometric and generative deep learning in tandem with novel applications of classic techniques in high-performance computing to build an approximate (or surrogate) model that is hundreds of times faster than traditional ice sheet models. Such a speedup will in turn allow researchers to explore an unprecedented range of future scenarios and meet the challenges of ice modeling for understanding future global impacts. In addition, a nine-day summer school will bring together instructors and students from around the globe to learn and share effective methods for applying deep learning to problems in glaciology. To help high schoolers learn the skills needed to understand climate change and its uncertainties and to use computers to tackle the associated scientific, economic, and policy challenges, we will develop educational modules tailored towards high school students exploring the intersection of programming, data, and global change.This project is co-funded by the Directorate for Geosciences to support AI/ML advancement in the geosciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.