Clouds are critical for weather and climate prediction. The multiple scales of cloud processes make simulation difficult. Often models and measurements are used to develop empirical relationships for large‐scale models to be computationally efficient. Machine learning provides another potential tool to improve our empirical parameterizations of clouds. To explore these opportunities, we replace the warm rain formation process in a General Circulation Model (GCM) with a detailed treatment from a bin microphysical model that causes a 400% slowdown in the GCM. We analyze the changes in climate that result from the use of the bin microphysical calculation and find improvements in the rain onset and frequency of light rain compared to high resolution process models and observations. We also find a resulting change in the cloud feedback response of the model to warming, which will significantly impact the climate sensitivity. We then replace the bin microphysical model with several neural networks designed to emulate the autoconversion and accretion rates produced by the bin microphysical model. The neural networks are organized into two stages: the first stage identifies where tendencies will be nonzero (and the sign of the tendency), and the second stage predicts the magnitude of the autoconversion and accretion rates. We describe the risks of overfitting, extrapolation, and linearization by using perfect model experiments with and without the emulator. We can recover the solutions with the emulators in almost all respects, and get simulations that perform as the detailed model, but with the computational cost of the control simulation.

中文翻译：

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机器学习暖雨过程

云对天气和气候预测至关重要。云过程的多个规模使仿真变得困难。通常使用模型和度量来建立大型模型的经验关系，以提高计算效率。机器学习提供了另一种潜在的工具来改善我们的云经验参数化。为了探索这些机会，我们用bin微物理模型中的详细处理方法替换了一般循环模型（GCM）中的暖雨形成过程，该处理导​​致了GCM速度降低了400％。我们分析了利用箱式微物理计算得出的气候变化，并发现与高分辨率过程模型和观测结果相比，降雨的发生和小雨频率的改善。我们还发现模型对气候变暖的云反馈响应发生了变化，这将显着影响气候敏感性。然后，我们用几个神经网络代替bin微物理模型，这些神经网络旨在模拟bin微物理模型产生的自动转换和吸积率。神经网络分为两个阶段：第一个阶段识别趋势将为非零的位置（以及趋势的符号），第二个阶段预测自动转换和吸积率的大小。我们通过使用带有和不带有仿真器的完美模型实验来描述过度拟合，外推和线性化的风险。我们几乎可以在所有方面使用仿真器恢复解决方案，并获得作为详细模型执行的仿真，