A multistage error growth conceptual model for mesoscale predictability

A recent study by the authors examined the predictability of an idealized baroclinic wave amplifying in a conditionally unstable atmosphere through numerical simulations with parameterized moist convection. It was demonstrated that with the effect of moisture included, the error starting from small random noise is characterized by upscale growth in the short-term forecast of a rapidly growing synoptic-scale disturbance. The current study seeks to further explore the mesoscale error growth dynamics in the idealized moist baroclinic waves through cloud-resolving experiments with model grid increments down to 3.3 km.

A multistage error growth conceptual model is proposed. In the initial stage, the errors first grow from small-scale convective instability and then quickly saturate at the convective scales on time scales of O(1 h). The amplitude of saturation errors may be a function of CAPE with its areal coverage determined by large-scale flows. In the transitional stage, the errors transform from convective-scale unbalanced motions to larger-scale balanced motions through geostrophic adjustment. Part of the errors due to difference in latent heating from convection may be retained in the balance fields while the others are radiating away in the form of gravity waves. In the final stage, the balanced components of the errors project onto the larger-scale flow and grow with the background baroclinic instability. Though an examination of the difference error energy budget, similar to the turbulence kinetic energy budget analysis, it is found that buoyancy production due mostly to moist convection is comparable to shear production due to nonlinear advection. Not only does turning off latent heating dramatically decrease buoyancy production, but it also reduces shear production (nonlinear advection) to less than 20% of its original amplitude. These new findings further demonstrate of the impacts of moist convection and diabatic heating on the limit of mesoscale predictability.