and memory compared to the conventional double-precision calculations [@palem2014inexactness; @duben2015opportunities; @leyffer2016doing]. In the past few years, the rapid algorithmic advances in artificial intelligence (AI), and in particular data-driven modeling, have been explored for improving the simulation and prediction of nonlinear dynamical systems [@schneider2017earth; @kutz2017deep; @gentine2018could; @moosavi2018machine; @wu2019enforcing; @toms2019deep; @brunton2019data; @duraisamy2019turbulence; @reichstein2019deep; @lim2019predicting; @scher2019generalization]. One appeal of the data-driven approach is that fast/accurate data-driven surrogate models that are trained on data from high-fidelity, computationally demanding simulations can be used to accelerate and/or improve the prediction and simulation of complex dynamical systems. Furthermore, for some poorly understood processes for which observational data are available (e.g., clouds), data-driven surrogate models built using such data might potentially outperform physics-based surrogate models [@schneider2017earth; @reichstein2019deep]. Recent studies have shown promising results in using AI to build data-driven parameterizations for modeling of some atmospheric and oceanic processes [@rasp2018deep; @brenowitz2018prognostic; @gagne2019machine; @o2018using; @bolton2019applications; @dueben2018challenges; @watson2019applying; @salehipour2019deep]. In the turbulence and dynamical systems communities, similarly encouraging outcomes have been reported [@ling2016reynolds; @mcdermott2017ensemble; @pathak2018model; @rudy2018deep; @vlachas2018data; @mohan2019compressed; @wu2019enforcing; @raissi2019physics; @zhu2019physics; @mcdermott2019deep] .

*The objective of the current study is to make progress toward addressing the following question: Which AI-based data-driven technique(s) can best predict the spatio-temporal evolution of a multi-scale 