Predictable internal climate variability on decadal timescales (2–10 years) is associated with large-scale oceanic processes, however these predictable signals may be masked by the noisy climate system. One approach to overcoming this problem is investigating state-dependent predictability—how differences in prediction skill depend on the initial state of the system. We present a machine learning approach to identify state-dependent predictability on decadal timescales in the Community Earth System Model version 2 pre-industrial control simulation by incorporating uncertainty estimates into a regression neural network. We leverage the network's prediction of uncertainty to examine state dependent predictability in sea surface temperatures by focusing on predictions with the lowest uncertainty outputs. In particular, we study two regions of the global ocean—the North Atlantic and North Pacific—and find that skillful initial states identified by the neural network correspond to particular phases of Atlantic multi-decadal variability and the interdecadal Pacific oscillation.

中文翻译：

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将不确定性纳入回归神经网络可以识别 CESM2 中的年代际状态相关可预测性

十年时间尺度（2-10 年）上可预测的内部气候变率与大规模海洋过程有关，但是这些可预测的信号可能会被嘈杂的气候系统所掩盖。克服这个问题的一种方法是研究依赖于状态的可预测性——预测技能的差异如何取决于系统的初始状态。我们提出了一种机器学习方法，通过将不确定性估计合并到回归神经网络中来识别社区地球系统模型第 2 版工业前控制模拟中十年时间尺度上的状态相关可预测性。我们利用网络对不确定性的预测，通过关注具有最低不确定性输出的预测来检查海表温度的状态相关可预测性。尤其是，