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Do Regional Climate Models Realistically Represent Precipitation?

Reference
Soares, P.M.M., Cardoso, R.M., Miranda, P.M.A., Viterbo, P. and Belo-Pereira, M. 2012. Assessment of the ENSEMBLES regional climate models in the representation of precipitation variability and extremes over Portugal. Journal of Geophysical Research 117: 10.1029/2011JD016768.
Soares et al. (2012) write in introducing their study that "Regional Climate Models (RCMs) are increasingly used to assess the impact of climate change at regional and local scales (Giorgi and Mearns, 1999; Wang et al., 2004; Christensen and Christensen, 2007)," because "in regions where local features affecting the atmospheric flow, such as topography and coastal processes, are prevalent, finer resolution simulations with state-of-the-art mesoscale models are required to reproduce observed weather and climate (Mass et al., 2002; Salathe et al., 2008)." Against this backdrop, Soares et al. utilized "a new data set of daily gridded observations of precipitation, computed from over 400 stations in Portugal, to assess the performance of 12 regional climate models at 25-km resolution, from the ENSEMBLES set, all forced by ERA-40 boundary conditions, for the 1961-2000 period," while "standard point error statistics, calculated from grid point and basin aggregated data, and precipitation related climate indices are used to analyze the performance of the different models in representing the main spatial and temporal features of the regional climate, and its extreme events." And what did they find?

Although the five Portuguese researchers say that the models achieved what they call a "good representation" of the features listed above, they also list a number of less-than-hoped-for results: (1) "10 of the 12 analyzed models under-predict Portuguese precipitation," (2) "half of the models under-represent observed variability of daily precipitation," (3) "models were found to underestimate the number of wet days," (4) "grid point percentiles of precipitation are generally under-predicted," (5) "in all cases, there is a significant model spread," (6) "the 95th percentile is under-predicted by all models in most of the country," and (7) "there is an important model spread in all analyzed variables."

So, are we there yet? ... as the saying goes? Unfortunately, as Soares et al. state in their concluding paragraph, "the present results suggest that there is still some way to go in this research."

Additional References
Christensen, J.H. and Christensen, O.B. 2007. A summary of the PRUDENCE model projections of changes in European climate by the end of this century. Climatic Change 81: 7-30.

Giorgi, F. and Mearns, L.O. 1999. Introduction to special section: Regional climate modeling revisited. Journal of Geophysical Research 104: 6335-6352.

Mass, C.F., Ovens, D., Westrick, K. and Colle, B.A. 2002. Does increasing horizontal resolution produce more skillful forecasts? Bulletin of the American Meteorological Society 83: 407-430.

Salathe, E.P., Steed, R., Mass, C.F. and Zahn, P.H. 2008. A high-resolution climate model for the United States Pacific Northwest: Mesoscale feedbacks and local responses to climate change. Journal of Climate 21: 5708-5726.

Wang, Y., Leung, L.R., McGregor, J.L., Lee, D.K., Wang, W.C., Ding, Y. and Kimura, F. 2004. Regional climate modeling: Progress, challenges and prospects. Journal of the Meteorological Society of Japan 82: 1599-1628.

Archived 28 August 2012