A feature based perspective on upscale error growth.

Atmospheric predictability is fundamentally limited by the upscale growth of initial small-scale, small-amplitude errors. Studying upscale error growth mechanism is essential to better understand this fundamental limitation. Upscale error growth is frequently investigated by spectral analysis. By design, however, spectral analysis is non-local. A local investigation of error growth in different flow configurations is desirable, though, to study the well-known flow dependence of error growth. We thus take here a complementary approach to spectral analysis and identify local regions of prominent errors as “error features”.

We have developed an automated algorithm to identify error features in gridded data and track their spatial and temporal evolution. Errors are considered in terms of potential vorticity (PV) and near the tropopause, where they maximize. A previously derived PV-error tendency equation is evaluated to quantify the different contributions to error growth in previously published upscale error growth experiments with the global prediction Model ICON from the German Weather Service. Errors in these experiments grow from very small initial-condition uncertainty (three orders of magnitude smaller than current-day uncertainty) and due to differences in the seeding of a stochastic convection scheme.

Spatial composites centered on the centroid of error features indicate that features are primarily generated ahead of an upper-tropospheric trough. The environment surrounding the features at the time of their first detection is characterized by locally enhanced lower to mid tropospheric moisture, latent heat release, and upper tropospheric divergence. Subsequently, this moist-diabatic nature of the error environment becomes gradually less prominent. The evaluating of process specific error growth rates enables to quantify the upscale growth mechanics in more detail. For this purpose, we integrate the growth rates over the respective area associated with an error feature. Examination of the combined growth rates of all features reproduces the previously found three-phased multi-scale upscale growth paradigm: Errors are first generated on the small scale by differences in latent heat release, then projected onto the tropopause region by associated differences in upper tropospheric divergent outflow, and finally amplified by nonlinear Rossby wave dynamics. The growth rates from a single feature, however, can substantially differ from the mean picture. Some features, e.g., go through the described stages in a cyclic sequence, and the main focus of the presentation will be on the differences between fast and slowly amplifying error features.