Incorporating Uncertainty Into a Regression Neural Network Enables Identification of Decadal State‐Dependent Predictability in CESM2
Incorporating Uncertainty Into a Regression Neural Network Enables Identification of Decadal State‐Dependent Predictability in CESM2
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DOI：
10.1029/2022gl098635
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发表时间：
2022-08
影响因子：
5.2
通讯作者：
Emily M. Gordon;E. Barnes
中科院分区：
文献类型：
--
作者：
Emily M. Gordon;E. Barnes
在十年时间尺度（2-10年）上可预测的内部气候变率与大尺度海洋过程有关，但这些可预测的信号可能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状态相关的可预测性--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纳入回归神经网络，在社区地球系统模型第2版工业化前控制模拟中识别十年时间尺度上的状态依赖可预测性。我们利用网络的预测的不确定性，以检查状态依赖的可预测性，在海面温度的预测与最低的不确定性输出的重点。特别是，我们研究了全球海洋的两个区域-北大西洋和北太平洋-并发现神经网络识别的熟练初始状态对应于大西洋年代际变化和年代际太平洋振荡的特定阶段。
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.