Machine Learning has become a pervasive tool in climate science applications. However, current models fail to address nonstationarity induced by anthropogenic alterations in greenhouse emissions and do not routinely quantify the uncertainty of proposed projections. In this paper, we model the Atlantic Meridional Overturning Circulation (AMOC) which is of major importance to climate in Europe and the US East Coast by transporting warm water to these regions, and has the potential for abrupt collapse. We can generate arbitrarily extreme climate scenarios through arbitrary time scales which we then predict using neural networks. Our analysis shows that the AMOC is predictable using neural networks under a diverse set of climate scenarios. Further experiments reveal that MLPs and Deep Ensembles can learn the physics of the AMOC instead of imitating its progression through autocorrelation. With quantified uncertainty, an intriguing pattern of "spikes" before critical points of collapse in the AMOC casts doubt on previous analyses that predicted an AMOC collapse within this century. Our results show that Bayesian Neural Networks perform poorly compared to more dense architectures and care should be taken when applying neural networks to nonstationary scenarios such as climate projections. Further, our results highlight that big NN models might have difficulty in modeling global Earth System dynamics accurately and be successfully applied in nonstationary climate scenarios due to the physics being challenging for neural networks to capture.

翻译：机器学习已成为气候科学应用中的普遍工具。然而，当前模型未能解决由人为温室气体排放变化引起的非平稳性，也未常规量化所提出预测的不确定性。本文对北大西洋经向翻转环流（AMOC）进行建模——该环流通过向欧洲和美国东海岸输送暖水，对这两个地区的气候具有重大影响，并存在突然崩溃的可能性。我们能够生成任意时间尺度下的极端气候情景，并利用神经网络进行预测。分析表明，在多种气候情景下，AMOC具有可预测性。进一步实验揭示，多层感知机（MLPs）与深度集成（Deep Ensembles）能够学习AMOC的物理过程，而非通过自相关模仿其演变。在量化不确定性的条件下，AMOC临界崩溃点前出现的“尖峰”异常模式，对先前预测本世纪内AMOC将崩溃的分析提出了质疑。我们的结果表明，贝叶斯神经网络（Bayesian Neural Networks）相较于更密集的架构表现较差，在将神经网络应用于气候预测等非平稳情景时需谨慎。此外，我们的研究凸显了大型神经网络模型可能难以准确模拟全球地球系统动力学，并因神经网络的物理捕捉能力受限而难以成功应用于非平稳气候情景。