New Research Paper “Assessment Of Three Dynamical Climate Downscaling Methods”

Lo, J., Z. Yang, and R. A. Pielke Sr. (2008): Assessment of dynamical climate downscaling methods using the Weather Research and Forecasting (WRF) Model. J. Geophys. Res.,  J. Geophys. Res., doi:10.1029/2007JD009216, in press.

The abstract reads

The common methodology in dynamical regional climate downscaling employs a continuous integration of a limited-area model with a single initialization of the atmospheric fields and frequent updates of lateral boundary conditions based on general circulation model outputs or reanalysis datasets. This study suggests alternative methods that can be more skillful than the traditional one in obtaining high-resolution climate information. We use the Weather Research and Forecasting (WRF) model with a grid spacing at 36 km over the conterminous U.S. to dynamically downscale the 1-degree NCEP Global Final Analysis (FNL). We perform three types of experiments for the entire year of 2000: 1) continuous integrations with a single initialization as usually done, 2) consecutive integrations with frequent re-initializations, and 3) as 1) but with a 3-D nudging being applied. The simulations are evaluated in a high temporal scale (6-hourly) by comparison with the 32-km NCEP North American Regional Reanalysis (NARR). Compared to NARR, the downscaling simulation using the 3-D nudging shows the highest skill, and the continuous run produces the lowest skill. While the re-initialization runs give an intermediate skill, a run with a more frequent (e.g. weekly) re-initialization outperforms that with the less frequent re-initialization (e.g. monthly). Dynamical downscaling outperforms bi-linear interpolation, especially for meteorological fields near the surface over the mountainous regions. The 3-D nudging generates realistic regional  scale patterns that are not resolved by simply updating the lateral boundary conditions as done traditionally, therefore significantly improving the accuracy of generating regional climate information.”

This paper has very important implications in terms of providing regional and local climate prediction information to policymakers and others. It further confirms our conclusions in the paper

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.

Since the results deteriorate when the driving large scale atmospheric information is applied only at the lateral boundaries, this means that the regional model is a slave to the parent global scale information. If the global model has errors, it is not possible for the regional model to correct for these errors.  Using  regional and local scale predictions based on dynamic downscaling from multi-decadal global climate model projections to make policy decisions decades into the future, therefore, is  erroneous and misleading.  Those who disagree with this conclusion need to provide quantitative tests that should be used to assess whether dynamic downscaling can predict regional weather patterns in the coming years (such as drought events) that provides skillful and useful information to policymakers. Certainly the forecasts for the winter 2007/2008 in the western USA have been a major bust.

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