Regional Climate Models (RCMs) are used extensively to provide regional and local weather, water resource and other predictions, decades into the future, to planners and policymakers. The RCMs use lateral boundary conditions and interior domain nudging from the multi-decadal global climate model predictions (e.g. see Chapter 7 in the 2007 IPCC report).
As concluded in our previous papers, however, the presentation of regional forecast skill is an illusion based on the finer scale appearing results produced by the RCMs, as a consequence of their ability to map their output onto more detailed resolution terrain and landscape. The RCMs, however, are just sophsiticated interpolations of the output from the global climate models, and cannot correct biases that are already present in these models. Our past papers that discuss this issue include
Castro, C.L., R.A. Pielke Sr., and G. Leoncini, 2005: Dynamical downscaling: Assessment of value retained and added using the Regional Atmospheric Modeling System (RAMS). J. Geophys. Res. – Atmospheres, 110, No. D5, D05108, doi:10.1029/2004JD004721
Castro, C.L., R.A. Pielke Sr., J. Adegoke, S.D. Schubert, and P.J. Pegion, 2007: Investigation of the summer climate of the contiguous U.S. and Mexico using the Regional Atmospheric Modeling System (RAMS). Part II: Model climate variability. J. Climate, 20, 3866-3887.
Lo, J.C.-F., Z.-L. Yang, and R.A. Pielke Sr., 2008: Assessment of three dynamical climate downscaling methods using the Weather Research and Forecasting (WRF) Model. J. Geophys. Res., 113, D09112, doi:10.1029/2007JD009216.
We now have another paper which is “in press” that further documents the limitations of regional climate modeling. The paper is
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, J. Geophys. Res., doi:10.1029/2007JD009461, in press.
The abstract reads
“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. Signicantly 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.”
Our conclusions support the statement that
“….dynamical downscaling .. does not retain value of the large-scale over and above that which exists in the larger global reanalysis. If the variability of synoptic features is underestimated or there is a consistent bias in the larger model, no increased skill would be gained by dynamical downscaling”
What this means is that claims of regional forecast skill for decades into the future (such as changes in drought frequency, etc) represent an overselling of the capabilities of the RCMs since the global climate models not have all of the first order climate forcings and feedbacks (e.g. see), which necessarily is a prerequisite for skillful forecasts. Media articles and published papers that claim otherwise are ignoring scientific evidence that shows that such regional forecast ability does not yet exist.
Climate Science: Roger Pielke Sr. 