Title: Reservoir Computing as a Tool for Climate Predictability Studies Show Chinese title

Title: 作为气候可预测性研究工具的储备计算

Abstract: Reduced-order dynamical models play a central role in developing our understanding of predictability of climate irrespective of whether we are dealing with the actual climate system or surrogate climate-models. In this context, the Linear-Inverse-Modeling (LIM) approach, by capturing a few essential interactions between dynamical components of the full system, has proven valuable in providing insights into predictability of the full system. We demonstrate that Reservoir Computing (RC), a form of learning suitable for systems with chaotic dynamics, provides an alternative nonlinear approach that improves on the predictive skill of the LIM approach. We do this in the example setting of predicting sea-surface-temperature in the North Atlantic in the pre-industrial control simulation of a popular earth system model, the Community-Earth-System-Model so that we can compare the performance of the new RC based approach with the traditional LIM approach both when learning data is plentiful and when such data is more limited. The improved predictive skill of the RC approach over a wide range of conditions -- larger number of retained EOF coefficients, extending well into the limited data regime, etc. -- suggests that this machine-learning technique may have a use in climate predictability studies. While the possibility of developing a climate emulator -- the ability to continue the evolution of the system on the attractor long after failing to be able to track the reference trajectory -- is demonstrated in the Lorenz-63 system, it is suggested that further development of the RC approach may permit such uses of the new approach in more realistic predictability studies. Show Chinese abstract

Abstract: 降阶动力学模型在发展我们对可预测性的理解中起着核心作用，无论我们处理的是实际的气候系统还是替代的气候模型。 在此背景下，线性逆建模（LIM）方法通过捕捉完整系统中动力学组件之间的一些关键相互作用，已被证明在提供对完整系统可预测性的见解方面非常有价值。 我们证明了储层计算（RC），一种适用于具有混沌动力学系统的学习方法，提供了一种替代的非线性方法，该方法在预测技能上优于LIM方法。 我们在一个示例设置中进行此操作，即在一个人气地球系统模型——社区地球系统模型的前工业控制模拟中预测北大西洋的海面温度，以便我们可以比较基于RC的新方法与传统LIM方法的性能，无论学习数据是充足还是有限的。 RC方法在广泛条件下的预测技能改进——保留更多的EOF系数，扩展到数据有限的领域等——表明这种机器学习技术可能在气候可预测性研究中有用。 虽然在洛伦兹-63系统中已经展示了开发气候模拟器的可能性——即在无法跟踪参考轨迹后，继续在吸引子上演化系统的能力——但建议RC方法的进一步发展可能会允许在更现实的可预测性研究中使用这种方法。