Decadal Predictability in a High-Resolution Eddy-Resolving Model: A Signal-to-Noise Paradox Perspective

Recent studies suggest the widespread existence of the signal-to-noise paradox in seasonal-to-decadal climate predictions. The essence of the paradox is that the signal-to-noise ratio in models can be unrealistically small and models may make better predictions of the observations than they predict themselves. Underestimated decadal predictability has been identified in current global climate models (e.g., IPCC-class models) and based on a multi-model assessment of CMIP5/6 models, we find that models tend to underestimate decadal predictability in regions where it is likely for the paradox to exist. These models fail to fully resolve mesoscale ocean features (with length scales on the order of 10 km), such as the western boundary currents, potentially contributing to the signal-to-noise paradox and thus limiting climate predictability over decadal timescales. To test this hypothesis, we perform a suite of CESM model experiments incorporating high-resolution eddy-resolving ocean (HR: 0.1°) in that capture these important mesoscale ocean features with increased fidelity. Compared with the eddy-parameterized ocean model (LR: 1°), the paradox is less likely to exist in HR, particularly over eddy-rich regions. These also happen to be regions where increased decadal predictability is identified in HR. We argue that this enhanced predictability is due to the enhanced vertical connectivity in the ocean. The presence of mesoscale ocean features and associated vertical connectivity significantly influence decadal variability, predictability, and the signal-to-noise paradox.

Moreover, we detect a better representation of the air-sea interactions between SST and low-level atmosphere over the Gulf Stream, thus improving low-frequency rainfall variations and extremes over the Southeast US. The results further imply that high-resolution GCMs with increased ocean model resolution may be needed in future climate prediction systems.