Indicator Patterns of Forced Change Learned by an Artificial Neural Network
Indicator Patterns of Forced Change Learned by an Artificial Neural Network
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DOI：
10.1029/2020ms002195
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发表时间：
2020-09-01
影响因子：
6.8
通讯作者：
Anderson, David
中科院分区：
文献类型：
--
作者：
Barnes, Elizabeth A.;Toms, Benjamin;Anderson, David
气候科学中的许多问题需要识别被内部气候变率的“噪音”和模型之间的差异所掩盖的信号。在以前的工作之后，我们训练了一个人工神经网络（ANN）来预测来自强迫气候模型模拟的给定年平均温度（或降水）图的年份。这个预测任务需要人工神经网络学习强迫模式的变化背景中的气候噪声和模型的差异。然后，我们应用神经网络可视化技术（分层相关传播）可视化的空间模式，导致人工神经网络成功地预测了这一年。因此，这些空间格局可作为被迫变化的“可靠指标”。人工神经网络的架构选择，使这些指标随时间变化，从而捕捉区域变化信号的演变性质。结果进行了比较，以获得直观的可靠指标识别的人工神经网络的信噪比和多元线性回归等更标准的方法。然后，我们应用一个额外的可视化工具（向后优化），以突出在模拟和观察到的变化模式的分歧是最重要的一年的预测。这项工作表明，人工神经网络及其可视化工具是提取强迫变化的气候模式的有力工具。
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.