Abstract/Summary

General circulation models (GCMs) have been criticized for their failure to represent the observed scales of precipitation, particularly in the tropics where simulated daily rainfall is too light, too frequent, and too persistent. Previous assessments have focused on temporally or spatially averaged precipitation, such as daily means or regional averages. These evaluations offer little actionable information for model developers, because the interactions between the resolved dynamics and parameterized physics that produce precipitation occur at the native gridscale and timestep. We introduce a set of diagnostics (ASoP1) to compare the spatial and temporal scales of precipitation across GCMs and observations, which can be applied to data ranging from the gridscale and timestep to regional and sub-monthly averages. ASoP1 measures the spectrum of precipitation intensity, temporal variability as a function of intensity, and spatial and temporal coherence. When applied to timestep, gridscale tropical precipitation from ten GCMs, the diagnostics reveal that far from the "dreary" persistent light rainfall implied by daily mean data, most models produce a broad range of timestep intensities that span 1-100 mm/day. Models show widely varying spatial and temporal scales of timestep precipitation. Several GCMs show concerning quasi-random behavior that may influence alter the spectrum of atmospheric waves. Averaging precipitation to a common spatial (~600 km) or temporal (3-hr) resolution substantially reduces variability among models, demonstrating that averaging hides a wealth of information about intrinsic model behavior. When compared against satellite-derived analyses at these scales, all models produce features that are too large and too persistent.