{"title":"Uncertainty Quantification of a Machine Learning Subgrid-Scale Parameterization for Atmospheric Gravity Waves","authors":"L. A. Mansfield, A. Sheshadri","doi":"10.1029/2024MS004292","DOIUrl":null,"url":null,"abstract":"<p>Subgrid-scale processes, such as atmospheric gravity waves (GWs), play a pivotal role in shaping the Earth's climate but cannot be explicitly resolved in climate models due to limitations on resolution. Instead, subgrid-scale parameterizations are used to capture their effects. Recently, machine learning (ML) has emerged as a promising approach to learn parameterizations. In this study, we explore uncertainties associated with a ML parameterization for atmospheric GWs. Focusing on the uncertainties in the training process (parametric uncertainty), we use an ensemble of neural networks to emulate an existing GW parameterization. We estimate both offline uncertainties in raw NN output and online uncertainties in climate model output, after the neural networks are coupled. We find that online parametric uncertainty contributes a significant source of uncertainty in climate model output that must be considered when introducing NN parameterizations. This uncertainty quantification provides valuable insights into the reliability and robustness of ML-based GW parameterizations, thus advancing our understanding of their potential applications in climate modeling.</p>","PeriodicalId":14881,"journal":{"name":"Journal of Advances in Modeling Earth Systems","volume":"16 7","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024MS004292","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Advances in Modeling Earth Systems","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1029/2024MS004292","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Subgrid-scale processes, such as atmospheric gravity waves (GWs), play a pivotal role in shaping the Earth's climate but cannot be explicitly resolved in climate models due to limitations on resolution. Instead, subgrid-scale parameterizations are used to capture their effects. Recently, machine learning (ML) has emerged as a promising approach to learn parameterizations. In this study, we explore uncertainties associated with a ML parameterization for atmospheric GWs. Focusing on the uncertainties in the training process (parametric uncertainty), we use an ensemble of neural networks to emulate an existing GW parameterization. We estimate both offline uncertainties in raw NN output and online uncertainties in climate model output, after the neural networks are coupled. We find that online parametric uncertainty contributes a significant source of uncertainty in climate model output that must be considered when introducing NN parameterizations. This uncertainty quantification provides valuable insights into the reliability and robustness of ML-based GW parameterizations, thus advancing our understanding of their potential applications in climate modeling.
大气重力波(GWs)等亚网格尺度过程在塑造地球气候方面发挥着关键作用,但由于分辨率的限制,无法在气候模式中明确解决。相反,亚网格尺度参数被用来捕捉它们的影响。最近,机器学习(ML)已成为学习参数化的一种有前途的方法。在本研究中,我们探讨了与大气全球变暖 ML 参数化相关的不确定性。针对训练过程中的不确定性(参数不确定性),我们使用神经网络集合来模拟现有的 GW 参数化。我们估算了原始神经网络输出中的离线不确定性,以及神经网络耦合后气候模式输出中的在线不确定性。我们发现,在线参数不确定性是气候模式输出不确定性的一个重要来源,在引入神经网络参数化时必须加以考虑。这种不确定性量化对基于 ML 的全球变暖参数化的可靠性和稳健性提供了宝贵的见解,从而推进了我们对其在气候建模中的潜在应用的理解。
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