A Spectral Nudging Technique for Dynamical Downscaling Purposes

TLDR

Spectral nudging imposes time‑variable large‑scale atmospheric states on a regional model, conditioning regional climate statistics on continental‑scale conditions and regional features such as marginal seas and mountain ranges. This study examines the performance of spectral nudging in an extended climate simulation. The regional model is forced to satisfy both boundary conditions and large‑scale flow conditions within the integration area. Spectral nudging keeps the large‑scale state close to the driving state while generating small‑scale features, whereas standard boundary forcing allows conflicting internal states, indicating that spectral nudging is a suboptimal indirect data assimilation technique.

Abstract

The “spectral nudging” method imposes time-variable large-scale atmospheric states on a regional atmospheric model. It is based on the idea that regional-scale climate statistics are conditioned by the interplay between continental-scale atmospheric conditions and such regional features as marginal seas and mountain ranges. Following this “downscaling” idea, the regional model is forced to satisfy not only boundary conditions, possibly in a boundary sponge region, but also large-scale flow conditions inside the integration area. In the present paper the performance of spectral nudging in an extended climate simulation is examined. Its success in keeping the simulated state close to the driving state at larger scales, while generating smaller-scale features is demonstrated, and it is also shown that the standard boundary forcing technique in current use allows the regional model to develop internal states conflicting with the large-scale state. It is concluded that spectral nudging may be seen as a suboptimal and indirect data assimilation technique.