Scale-selective nudging with a diffusion-based filter in the variable-resolution Model for Prediction Across Scales version 8.2.2

Abstract. Nudged “specified-dynamics” configurations are widely used to align atmospheric models with reanalysis, but their behaviour in unstructured variable-resolution (VR) global models remains poorly understood. Here we implement a diffusion-based spectral nudging scheme in the Model for Prediction Across Scales–Atmosphere (MPAS-A) on a global VR mesh refined over East Asia and evaluate its performance under two convection schemes (Grell–Fritsch and Tiedtke) and a range of filter scales and nudged variables. Full analysis nudging imposes the strongest large-scale constraint and largely erases the scheme-dependent differences, whereas weaker, scale-selective spectral nudging still controls the large scales but allows GF and TK to exhibit distinct behaviours in precipitation frequency and rainband evolution. Kinetic-energy spectra, transient-eddy coherence, and temporal amplitude spectra jointly confirm that the diffusion-based filters act in a clearly scale-selective manner. Overall, our findings suggest that carefully tuned spectral nudging offers an effective trade-off: it keeps the large-scale flow phase-locked to the analysis while preserving enough variability to diagnose how different physics schemes shape the solution.