Reservoir Computing as a Tool for Climate Predictability Studies

Abstract

<p>  Reduced-order dynamical models play a central role in developing our<br>  understanding of predictability of climate irrespective of whether<br>  we are dealing with the actual climate system or surrogate climate<br>  models. In this context, the Linear Inverse Modeling (LIM) approach,<br>  by helping capture a few essential interactions between dynamical<br>  components of the full system, has proven valuable in being able to<br>  provide insights into the dynamical behavior of the full system.</p><p>  We demonstrate that Reservoir Computing (RC), a form of machine<br>  learning suited for learning in the context of chaotic dynamics,<br>  provides an alternative nonlinear approach that improves on the LIM<br>  approach. We do this in the example setting of predicting sea<br>  surface temperature in the North Atlantic in the pre-industrial<br>  control simulation of a popular earth system model, the Community<br>  Earth System Model version 2 (CESM2) so that we can compare the<br>  performance of the new RC based approach with the traditional LIM<br>  approach both when learning data is plentiful and when such data is<br>  more limited. The useful predictive skill of the RC approach over a<br>  wider range of conditions---larger number of retained EOF<br>  coefficients, extending well into the limited data regime,<br>  etc.---suggests that this machine learning approach may have a use<br>  in climate predictability studies. While the possibility of<br>  developing a climate emulator---the ability to continue the<br>  evolution of the system on the attractor long after failing to be<br>  able to track the reference trajectory---is demonstrated in context<br>  of the Lorenz-63 system, it is suggested that further development of<br>  the RC approach may permit such uses of the new approach in settings<br>  of relevance to realistic predictability studies.</p>