[1] The value restored and added by dynamical downscaling is quantitatively evaluated by considering the spectral behavior of the Regional Atmospheric Modeling System (RAMS) in relation to its domain size and grid spacing. A regional climate model (RCM) simulation is compared with NCEP Reanalysis data regridded to the RAMS grid at each model analysis time for a set of six basic experiments. At large scales, RAMS underestimates atmospheric variability as determined by the column integrated kinetic energy and integrated moisture flux convergence. As the grid spacing increases or domain size increases, the underestimation of atmospheric variability at large scales worsens. The model simulated evolution of the kinetic energy relative to the reanalysis regridded kinetic energy exhibits a decrease with time, which is more pronounced with larger grid spacing. Additional follow-on experiments confirm that the surface boundary forcing is the dominant factor in generating atmospheric variability for small-scale features and that it exerts greater control on the RCM solution as the influence of lateral boundary conditions diminish. The sensitivity to surface forcing is also influenced by the model parameterizations, as demonstrated by using a different convection scheme. For the particular case considered, dynamical downscaling with RAMS in RCM mode does not retain value of the large scale which exists in the larger global reanalysis. The utility of the RCM, or value added, is to resolve the smaller-scale features which have a greater dependence on the surface boundary. This conclusion regarding RAMS is expected to be true for other RCMs as well.

[1] In this paper, we compare the retained and added variability obtained using the regional climate model CLM (Climate version of the Local Model of the German Weather Service) to an earlier study using the RAMS (Regional Atmospheric Modeling System) model. Both models yield similar results for their standard configurations with a commonly used nudging technique applied to the driving model fields. Significantly both models do not adequately retain the large-scale variability in total kinetic energy with results poorer on a larger grid domain. Additional experiments with interior nudging, however, permit the retention of large-scale values for both models. The spectral nudging technique permits more added variability at smaller scales than a four-dimensional internal grid nudging on large domains. We also confirmed that dynamic downscaling does not retain (or increase) simulation skill of the large-scale fields over and beyond that which exists in the larger-scale model or reanalysis. Our conclusions should be relevant to all applications of dynamic downscaling for regional climate simulations.Citation: Rockel, B., C. L. Castro, R. A. Pielke Sr., H. von Storch, and G. Leoncini (2008), Dynamical downscaling: Assessment of model system dependent retained and added variability for two different regional climate models,

With movement toward kilometer-scale ensembles, new techniques are needed for their characterization. A new methodology is presented for detailed spatial ensemble characterization using the fractions skill score (FSS). To evaluate spatial forecast differences, the average and standard deviation are taken of the FSS calculated over all ensemble member-member pairs at different scales and lead times. These methods were found to give important information about the ensemble behavior allowing the identification of useful spatial scales, spinup times for the model, and upscale growth of errors and forecast differences. The ensemble spread was found to be highly dependent on the spatial scales considered and the threshold applied to the field. High thresholds picked out localized and intense values that gave large temporal variability in ensemble spread: local processes and undersampling dominate for these thresholds. For lower thresholds the ensemble spread increases with time as differences between the ensemble members upscale. Two convective cases were investigated based on the Met Office United Model run at 2.2-km resolution. Different ensemble types were considered: ensembles produced using the Met Office Global and Regional Ensemble Prediction System (MOGREPS) and an ensemble produced using different model physics configurations. Comparison of the MOGREPS and multiphysics ensembles demonstrated the utility of spatial ensemble evaluation techniques for assessing the impact of different perturbation strategies and the need for assessing spread at different, believable, spatial scales.

Convective-scale ensemble simulations with perturbed initial and lateral boundary conditions are performed to investigate the mechanisms and sensitivities of a central European convection event from the Convective and Orographically Induced Precipitation Study (COPS). In this event, a ''primary'' squall line developed ahead of a decaying mesoscale convective system (MCS) upstream of the Vosges Mountains (France), weakened over the Rhine valley, then regenerated as a ''secondary'' squall line over the Black Forest Mountains (Germany). All ensemble members captured the squall-line evolution, but most suffered from a delay in the onset of convection and positional errors of 50-150 km over the COPS region. These errors in the secondary initiation were linked to errors in the primary initiation. Detailed analysis revealed a similar primary initiation mechanism in all members: in the ascending branch of a midlevel frontal circulation ahead of the MCS, convection initiated within a mesoscale moisture anomaly embedded within the prefrontal flow. The differences in the skill of the ensemble members were related to subtle differences in their initial upper-level representation of potential vorticity (PV). Members that verified well possessed a stronger PV gradient at the leading edge of an upper-level trough. This led to more rapid cyclogenesis over northern France and the United Kingdom and faster development and propagation of the midlevel front and the prefrontal moisture anomaly. As a consequence, the squall lines in these members developed earlier and closer to the COPS region. This case study provides an example of the subtle mechanisms by which errors on the larger scales may transfer to the convective scale and lead to errors in quantitative precipitation forecasts.

The direct effects (within one time step) of the perturbations are to generate propagating Lamb and acoustic waves and produce generally small changes in cloud parameters and convective instability. In exceptional cases a perturbation at a specific grid point leads to switching of the diagnosed boundary-layer type or discontinuous changes in convective instability, through the generation or removal of a lid. The indirect effects (during the entire simulation) are changes in the intensity and location of precipitation and in the cloud-size distribution. Qualitatively different behaviour is found for strong (1 K amplitude) and weak (0.01 K amplitude) perturbations, with faster growth after sunrise found only for the weaker perturbations. However, the overall perturbation growth (as measured by the root-mean-square error of accumulated precipitation) reaches similar values at saturation, regardless of the perturbation characterization.