Errors of Interannual Variability and Trend in Dynamical Downscaling of Reanalysis

The interannual variability of dynamically downscaled analysis and its error relative to global coarse resolution analysis is examined in this paper. The regional model error is shown to significantly contaminate the interannual variability of the seasonal mean. The error occupies a significant part of the interannual variability, particularly during the summer season. Accordingly, the leading modes of empirical orthogonal functions (EOFs) of 500 hPa height in the region differ greatly from those of global analysis. In this paper a variant of spectral nudging, the scale selective bias correction (SSBC) method, is refined to further reduce the error within the observational error. Application of this method in dynamical downscaling reduced the error of the interannual variability of analysis fields (namely, height, temperature, and winds), and made the EOFs of seasonal mean at 500 hPa height agree well with those of the global analysis. Application of the SSBC had a modest impact on model-derived fields, such as precipitation and near-surface air temperature. The improvements in these fields are not as dramatic as those in the analysis fields, but the increased simulation skill is evident. A possible cause of the error in the interannual variability is discussed. No apparent systematic reduction in high-frequency variability is found, and the error in interannual variability is more likely due to excitation of the stationary computational mode by the lateral boundary forcing and ill-posed lateral boundary condition.