Dynamical downscaling: Assessment of value retained and added using the Regional Atmospheric Modeling System (RAMS)

Abstract

[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.

Multiscale Performance of the ALARO-0 Model for Simulating Extreme Summer Precipitation Climatology in Belgium

“…However, studies show that RCMs do not necessarily improve their driving GCM simulations or global reanalyses (e.g., Castro et al 2005;Jacob et al 2007;Sylla et al 2010). The use of nested LAMs or RCMs as a climate downscaling technique, indeed, involves a number of issues, one of which is related to the lateral boundary conditions (LBCs) (Giorgi and Mearns 1999;Denis et al 2002).…”

Projected Change—Models and Methodology

“…While the spectral nudging technique is now becoming more popular (e.g. Miguez-Macho et al 2004;Castro et al 2005), a debate on this technique is still ongoing; however, improvements through the application of spectral nudging are evident when the driving model represents a realistic large-scale flow, such as using reanalysis data as the driving model (Winterfeldt and Weisse 2009). In contrast, in the situation with a coarse GCM having an unrealistic large-scale circulation (caused by poorly represented topography due to the coarse resolution) as the driving model, even an RCM using spectral nudging could not alter the prescribed large-scale flow.…”

“…For many variables, the explicit treatment of small-scale atmospheric features leads to added value (AV) with respect to the driving model. Assessment of the AV of large-scale constrained versus unconstrained simulations was discussed by Castro et al (2005) and Rockel et al (2008). For different realisations of an RCM simulation, generated by small changes in the set-up of the RCM (e.g.…”

Climate version of the ETA regional forecast model

“…In order to be considered as a valid tool for dynamical downscaling of low-resolution GCM fields a regional climate model has to satisfy some requirements (e.g., Wang et al 2004;Castro et al 2005;Laprise, 2006). Firstly, it is needed to show that RCM is able to reproduce the principal features of the large-scale fields of GCM which data is used as driving boundary conditions and its main statistics.…”