The parameterization of moist convection contributes to uncertainty in climate modeling and numerical weather prediction. Machine learning (ML) can be used to learn new parameterizations directly from high‐resolution model output, but it remains poorly understood how such parameterizations behave when fully coupled in a general circulation model (GCM) and whether they are useful for simulations of climate change or extreme events. Here we focus on these issues using idealized tests in which an ML‐based parameterization is trained on output from a conventional parameterization and its performance is assessed in simulations with a GCM. We use an ensemble of decision trees (random forest) as the ML algorithm, and this has the advantage that it automatically ensures conservation of energy and nonnegativity of surface precipitation. The GCM with the ML convective parameterization runs stably and accurately captures important climate statistics including precipitation extremes without the need for special training on extremes. Climate change between a control climate and a warm climate is not captured if the ML parameterization is only trained on the control climate, but it is captured if the training includes samples from both climates. Remarkably, climate change is also captured when training only on the warm climate, and this is because the extratropics of the warm climate provides training samples for the tropics of the control climate. In addition to being potentially useful for the simulation of climate, we show that ML parameterizations can be interrogated to provide diagnostics of the interaction between convection and the large‐scale environment.

中文翻译：

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使用机器学习对湿对流进行参数化：气候，气候变化和极端事件建模的潜力

湿对流的参数化导致气候模型和数值天气预报的不确定性。机器学习（ML）可用于直接从高分辨率模型输出中学习新的参数化设置，但人们对这种参数化在通用循环模型（GCM）中完全耦合时的行为方式以及对模拟气候变化是否有用的了解仍然很少或极端事件。在这里，我们使用理想化测试关注这些问题，其中基于常规参数化的输出训练基于ML的参数化，并在GCM仿真中评估其性能。我们将决策树（随机森林）作为ML算法，它的优点是可以自动确保能量的节约和地表降水的非负性。具有ML对流参数化功能的GCM可以稳定运行，并且可以准确捕获重要的气候统计信息，包括极端降水，而无需进行特殊的极端培训。如果仅在控制气候条件下训练ML参数设置，则不会捕获控制气候与温暖气候之间的气候变化，但是如果训练包括来自两个气候的样本，则可以捕获到。值得注意的是，仅在温暖气候条件下进行训练时，也会捕获到气候变化，这是因为温暖气候的温带生物为控制气候的热带地区提供了训练样本。除了可能对气候模拟有用之外，我们还表明可以对ML参数化进行查询，以提供对流与大规模环境之间相互作用的诊断信息。