Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2020GL088376 |
Data‐driven Equation Discovery of Ocean Mesoscale Closures | |
Laure Zanna; Thomas Bolton | |
2020-08-06 | |
发表期刊 | Geophysical Research Letters
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出版年 | 2020 |
英文摘要 | The resolution of climate models is limited by computational cost. Therefore, we must rely on parameterizations to represent processes occurring below the scale resolved by the models. Here, we focus on parameterizations of ocean mesoscale eddies and employ machine learning (ML), namely relevance vector machines (RVM) and convolutional neural networks (CNN), to derive computationally efficient parameterizations from data, which are interpretable and/or encapsulate physics. In particular, we demonstrate the usefulness of the RVM algorithm to reveal closed‐form equations for eddy parameterizations with embedded conservation laws. When implemented in an idealized ocean model, all parameterizations improve the statistics of the coarse‐resolution simulation. The CNN is more stable than the RVM such that its skill in reproducing the high‐resolution simulation is higher than the other schemes; however, the RVM scheme is interpretable. This work shows the potential for new physics‐aware interpretable ML turbulence parameterizations for use in ocean climate models. |
领域 | 气候变化 |
URL | 查看原文 |
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文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/287673 |
专题 | 气候变化 |
推荐引用方式 GB/T 7714 | Laure Zanna,Thomas Bolton. Data‐driven Equation Discovery of Ocean Mesoscale Closures[J]. Geophysical Research Letters,2020. |
APA | Laure Zanna,&Thomas Bolton.(2020).Data‐driven Equation Discovery of Ocean Mesoscale Closures.Geophysical Research Letters. |
MLA | Laure Zanna,et al."Data‐driven Equation Discovery of Ocean Mesoscale Closures".Geophysical Research Letters (2020). |
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