Physics-Aware Multi-Task Learning for Atmospheric Turbulence Parameterization: Auxiliary Tasks versus Architectural Conditioning

Dynamic subgrid-scale (SGS) turbulence parameterizations in Large Eddy Simulation (LES) achieve superior physical fidelity but impose 2-4× computational overhead compared to static schemes, creating a critical bottleneck for high-resolution atmospheric modeling on HPC systems. Neural network based emulation offers a pathway to comparable accuracy at reduced computational cost, but realizing this potential requires architectures that generalize reliably across diverse atmospheric conditions and variable grid configurations.