Many problems in climate science require the identification of signals obscured by both the “noise” of internal climate variability and differences across models. Following previous work, we train an artificial neural network (ANN) to predict the year of a given map of annual‐mean temperature (or precipitation) from forced climate model simulations. This prediction task requires the ANN to learn forced patterns of change amidst a background of climate noise and model differences. We then apply a neural network visualization technique (layerwise relevance propagation) to visualize the spatial patterns that lead the ANN to successfully predict the year. These spatial patterns thus serve as “reliable indicators” of the forced change. The architecture of the ANN is chosen such that these indicators vary in time, thus capturing the evolving nature of regional signals of change. Results are compared to those of more standard approaches like signal‐to‐noise ratios and multilinear regression in order to gain intuition about the reliable indicators identified by the ANN. We then apply an additional visualization tool (backward optimization) to highlight where disagreements in simulated and observed patterns of change are most important for the prediction of the year. This work demonstrates that ANNs and their visualization tools make a powerful pair for extracting climate patterns of forced change.

中文翻译：

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人工神经网络学习的强迫变化指标模式

气候科学中的许多问题都需要识别被内部气候变异性的“噪声”和各个模型之间的差异所掩盖的信号。在先前的工作之后，我们训练了一个人工神经网络（ANN），以通过强制气候模型模拟来预测给定的年平均温度（或降水）图的年份。这项预测任务要求ANN在气候噪声和模型差异的背景下学习强制性的变化模式。然后，我们应用神经网络可视化技术（分层相关性传播）对导致ANN成功预测年份的空间模式进行可视化。因此，这些空间模式可作为强制变化的“可靠指标”。选择ANN的架构时，这些指标会随时间变化，因此捕捉了区域变化信号的不断发展的本质。将结果与信噪比和多线性回归等更标准方法的结果进行比较，以直观了解ANN所确定的可靠指标。然后，我们使用附加的可视化工具（向后优化）来突出显示模拟和观察到的变化模式中的差异对于年度预测最重要的地方。这项工作表明，人工神经网络及其可视化工具为提取强迫变化的气候模式提供了强大的一对。然后，我们使用附加的可视化工具（向后优化）来突出显示模拟和观察到的变化模式中的差异对于年度预测最重要的地方。这项工作表明，人工神经网络及其可视化工具为提取强迫变化的气候模式提供了有力的一对。然后，我们使用附加的可视化工具（向后优化）来突出显示模拟和观察到的变化模式中的差异对于年度预测最重要的地方。这项工作表明，人工神经网络及其可视化工具为提取强迫变化的气候模式提供了有力的一对。