# Ocean mesoscale variability

A clear comparison has emerged for AI substitutes for high-resolution ocean simulation: evolving states on a 1-degree grid and then spatially downscaling recovers only large-scale patterns, while learning dynamical evolution directly on a 0.1-degree grid preserves mesoscale energy, spectral characteristics, and frontal gradients. The work also provides a full-depth 1-degree emulator and a 0.1-degree sea surface temperature emulator, both stable over long-term free-running simulations without significant drift, giving the comparison reliable baselines.

A clear comparison has emerged for AI substitutes for high-resolution ocean simulation: evolving states on a 1-degree grid and then spatially downscaling recovers only large-scale patterns, while learning dynamical evolution directly on a 0.1-degree grid preserves mesoscale energy, spectral characteristics, and frontal gradients. The work also provides a full-depth 1-degree emulator and a 0.1-degree sea surface temperature emulator, both stable over long-term free-running simulations without significant drift, giving the comparison reliable baselines.