Process-Based Climate Model Development Harnessing Machine Learning: I. A Calibration Tool for Parameterization Improvement
Process-Based Climate Model Development Harnessing Machine Learning: I. A Calibration Tool for Parameterization Improvement
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DOI：
10.1029/2020ms002217
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发表时间：
2021-03-01
影响因子：
6.8
通讯作者：
Xu, Wenzhe
中科院分区：
文献类型：
--
作者：
Couvreux, Fleur;Hourdin, Frederic;Xu, Wenzhe
参数化的发展是天气和气候模式发展中的一项主要任务。在过去几十年中，由于难以将关键的物理过程纳入参数化，而且难以校准或“调整”其制定中涉及的许多自由参数，模型改进一直很缓慢。机器学习技术最近被用于加速开发过程。虽然一些研究建议用数据驱动的神经网络取代参数化，但我们主张保持物理参数化是气候预测可靠性的关键。在本文中，我们提出利用机器学习来改善物理参数化。特别是，我们使用高斯过程为基础的方法，从不确定性量化校准模型的自由参数在一个过程的水平。为了实现这一点，我们专注于比较单柱模拟和参考大涡模拟在多个边界层的情况下。我们的方法返回的自由参数的所有值与参考和任何结构的不确定性一致，允许一个减少域的可接受的值时，调整的三维（3D）的全球模型被考虑。该工具允许从植根于参数化公式的内在限制中找出由于参数校准不良而导致的缺陷。本文描述了在单列模式下进行调优的工具和原理。第2部分展示了基于过程的调优结果如何帮助进行3D全局模型调优。
The development of parameterizations is a major task in the development of weather and climate models. Model improvement has been slow in the past decades, due to the difficulty of encompassing key physical processes into parameterizations, but also of calibrating or "tuning" the many free parameters involved in their formulation. Machine learning techniques have been recently used for speeding up the development process. While some studies propose to replace parameterizations by data-driven neural networks, we rather advocate that keeping physical parameterizations is key for the reliability of climate projections. In this paper we propose to harness machine learning to improve physical parameterizations. In particular, we use Gaussian process-based methods from uncertainty quantification to calibrate the model free parameters at a process level. To achieve this, we focus on the comparison of single-column simulations and reference large-eddy simulations over multiple boundary-layer cases. Our method returns all values of the free parameters consistent with the references and any structural uncertainties, allowing a reduced domain of acceptable values to be considered when tuning the three-dimensional (3D) global model. This tool allows to disentangle deficiencies due to poor parameter calibration from intrinsic limits rooted in the parameterization formulations. This paper describes the tool and the philosophy of tuning in single-column mode. Part 2 shows how the results from our process-based tuning can help in the 3D global model tuning.