This paper describes an implementation of the Combined Hybrid-Parallel Prediction (CHyPP) approach of Wikner et al. (2020) on a low-resolution atmospheric global circulation model (AGCM). The CHyPP approach combines a physics-based numerical model of a dynamical system (e.g., the atmosphere) with a computationally efficient type of machine learning (ML) called reservoir computing (RC) to construct a hybrid model. This hybrid atmospheric model produces more accurate forecasts of most atmospheric state variables than the host AGCM for the first 7-8 forecast days, and for even longer times for the temperature and humidity near the earth’s surface. It also produces more accurate forecasts than a model based only on ML, or a model that combines linear regression, rather than ML, with the AGCM. The potential of the approach for climate research is demonstrated by a 10-year long hybrid model simulation of the atmospheric general circulation, which shows that the hybrid model can simulate the general circulation with substantially smaller systematic errors and more realistic variability than the host AGCM.

The paper investigates the applicability of machine learning (ML) to weather prediction by building a reservoir-computing-based, low-resolution, global prediction model. The model is designed to take advantage of the massively parallel architecture of a modern supercomputer. The forecast performance of the model is assessed by comparing it to that of daily climatology, persistence, and a numerical (physics-based) model of identical prognostic state variables and resolution. Hourly resolution 20-day forecasts with the model predict realistic values of the atmospheric state variables at all forecast times for the entire globe. The ML model outperforms both climatology and persistence for the first three forecast days in the midlatitudes, but not in the tropics. Compared to the numerical model, the ML model performs best for the state variables most affected by parameterized processes in the numerical model.

This study further evaluates the modeling approach of Jia et al. (2019) to investigate the potential effects of SST mesoscale variability on the atmospheric dynamics. The approach employs a global atmospheric circulation model coupled to a slab ocean model to produce two ensembles of simulations: one in which the ocean exhibits realistic SST mesoscale variability, and another in which the SST mesoscale variability is suppressed. The latter ensemble is produced by spatially filtering the SST analyses used for the estimation of the oceanic heat flux and the specification of the SST initial condition. The results of the present study, which focuses on the processes of the North Pacific, suggest that while the modeling approach yields the desired SST differences between the two ensembles at the mesoscales, it also introduces SST differences at the large scales that become the primary driver of the large scale differences in the simulated atmospheric flow. Diagnostics based on the eddy kinetic energy indicate that the large scale differences of the atmospheric flow lead to major differences in the dynamics of the jet stream and storm track. Because the large scale SST differences between the two ensembles are primarily driven by the differences between the prescribed estimates of the oceanic heat fluxes, finding a proper pair of those estimates is a necessary condition for the experiment design to detect the atmospheric response to SST mesoscale variability. The paper concludes with proposing a new strategy for the estimation of the oceanic heat fluxes.