Interpreting and Stabilizing Machine-Learning Parametrizations of Convection
Interpreting and Stabilizing Machine-Learning Parametrizations of Convection
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DOI：
10.1175/jas-d-20-0082.1
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发表时间：
2020-12-01
影响因子：
3.1
通讯作者：
Bretherton, Christopher S.
中科院分区：
文献类型：
--
作者：
Brenowitz, Noah D.;Beucler, Tom;Bretherton, Christopher S.
神经网络是在粗分辨率气候模型中对次网格尺度物理（例如，潮湿大气对流）进行参数化的一种有前景的技术，但它们缺乏可解释性和可靠性，这阻碍了其广泛应用。例如，人们尚未完全理解为什么神经网络参数化在与大气流体动力学耦合时经常会导致剧烈的不稳定性。本文介绍了针对参数化任务定制的用于解释其行为的工具。首先，我们评估神经网络对对流层低层稳定性和对流层中层湿度的非线性敏感度，这两者是对潮湿对流广泛研究的控制因素。其次，我们将这些神经网络的线性化响应函数与简化的重力波动力学相耦合，并通过解析方法诊断相应的相速度、增长率、波长和空间结构。为了证明它们的通用性，这些技术在两组神经网络上进行了测试，一组是用社区大气模型（Community Atmosphere Model，简称SPCAM）的超参数化版本进行训练的，另一组是用近全球云分辨模型（near - global cloud - resolving model，简称GCRM）进行训练的。尽管SPCAM模拟的气候比云分辨模型更暖，但两个神经网络都预测在潮湿和不稳定环境中会有更强的加热/干燥作用，这与观测结果一致。此外，频谱分析可以预测，当大气环流模型（General Circulation Models，简称GCMs）与支持不稳定且相速度大于5米/秒的重力波的网络耦合时会出现不稳定性。相比之下，驻波不稳定模式不会导致灾难性的不稳定。利用这些工具，分析了用SPCAM训练的神经网络与用GCRM训练的神经网络之间的差异，并揭示了逐步提高它们两者耦合在线性能的策略。
Neural networks are a promising technique for parameterizing subgrid-scale physics (e.g., moist atmospheric convection) in coarse-resolution climate models, but their lack of interpretability and reliability prevents widespread adoption. For instance, it is not fully understood why neural network parameterizations often cause dramatic instability when coupled to atmospheric fluid dynamics. This paper introduces tools for interpreting their behavior that are customized to the parameterization task. First, we assess the nonlinear sensitivity of a neural network to lower-tropospheric stability and the midtropospheric moisture, two widely studied controls of moist convection. Second, we couple the linearized response functions of these neural networks to simplified gravity wave dynamics, and analytically diagnose the corresponding phase speeds, growth rates, wavelengths, and spatial structures. To demonstrate their versatility, these techniques are tested on two sets of neural networks, one trained with a superparameterized version of the Community Atmosphere Model (SPCAM) and the second with a near-global cloud-resolving model (GCRM). Even though the SPCAM simulation has a warmer climate than the cloud-resolving model, both neural networks predict stronger heating/drying in moist and unstable environments, which is consistent with observations. Moreover, the spectral analysis can predict that instability occurs when GCMs are coupled to networks that support gravity waves that are unstable and have phase speeds larger than 5 m s(-1). In contrast, standing unstable modes do not cause catastrophic instability. Using these tools, differences between the SPCAM-trained versus GCRM-trained neural networks are analyzed, and strategies to incrementally improve both of their coupled online performance unveiled.