Machine learning of subgrid ocean physics for global ocean models

全球海洋模型的亚网格海洋物理机器学习

• 批准号: DE210100749
• 负责人: Dr Navid Constantinou
• 金额: $ 30.53万
• 依托单位: The Australian National University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Early Career Researcher Award
• 财政年份: 2021
• 资助国家: 澳大利亚
• 起止时间: 2021-05-28 至 2024-05-27
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE210100749
• 关键词: Machine; learning; subgrid; ocean; physics

Climate projections require simulations with ocean-climate models for hundreds of years. Computational resources limit the resolution of our models for such long runs, meaning that some key physical processes remain unresolved and must be parameterised. This project uses machine learning to find new parameterisations for unresolved ocean processes. These new parameterisations will be implemented into computationally cheaper coarse-resolution ocean models, thereby enhancing these models' representation of the ocean circulation. This project expects to reveal the dynamics of unresolved processes, to improve the accuracy of climate projections and to provide a proof-of-concept for how machine learning can be used in ocean and climate science.

气候预测需要用海洋气候模型进行数百年的模拟。计算资源限制了我们的模型在如此长时间运行时的分辨率，这意味着一些关键的物理过程仍然无法解决，必须参数化。该项目使用机器学习为未解决的海洋过程寻找新的参数化。这些新的参数化将被应用到计算成本更低的粗分辨率海洋模型中，从而增强这些模型对海洋环流的表征。该项目希望揭示未解决过程的动态，提高气候预测的准确性，并为机器学习如何用于海洋和气候科学提供概念验证。