A multimodel intercomparison of resolution effects on precipitation: simulations and theory
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An ensemble of six pairs of RCM experiments performed at 25 and 50 km for the period 1961–2000 over a large European domain is examined in order to evaluate the effects of resolution on the simulation of daily precipitation statistics. Application of the non-parametric two-sample Kolmorgorov–Smirnov test, which tests for differences in the location and shape of the probability distributions of two samples, shows that the distribution of daily precipitation differs between the pairs of simulations over most land areas in both summer and winter, with the strongest signal over southern Europe. Two-dimensional histograms reveal that precipitation intensity increases with resolution over almost the entire domain in both winter and summer. In addition, the 25 km simulations have more dry days than the 50 km simulations. The increase in dry days with resolution is indicative of an improvement in model performance at higher resolution, while the more intense precipitation exceeds observed values. The systematic increase in precipitation extremes with resolution across all models suggests that this response is fundamental to model formulation. Simple theoretical arguments suggest that fluid continuity, combined with the emergent scaling properties of the horizontal wind field, results in an increase in resolved vertical transport as grid spacing decreases. This increase in resolution-dependent vertical mass flux then drives an intensification of convergence and resolvable-scale precipitation as grid spacing decreases. This theoretical result could help explain the increasingly, and often anomalously, large stratiform contribution to total rainfall observed with increasing resolution in many regional and global models.
KeywordsRegional climate modeling Precipitation Model resolution
We thank two anonymous reviewers for their comments which greatly helped to improve the content, quality, and presentation of this manuscript. We acknowledge the ENSEMBLES project, funded by the European Commission’s 6th Framework Programme through Contract GOCE-CT-2003-505539. We acknowledge the climate dataset from the EU-FP6 project ENSEMBLES (http://www.ensembles-eu.org) and the data providers in the ECA and D project (http://eca.knmi.nl). This study was partly funded by the European Union FP6 project WATCH (Contract No. 036946). This research was supported by the Director, Office of Science, Office of Biological and Environmental Research of the U.S. Department of Energy Regional and Global Climate Modeling Program (RGCM) under Contract No. DE-AC02-05CH11231. We thank all of the participating modeling groups for providing the data. We thank Malcolm Haylock and Albert Klein Tank for answering questions about the ENSEMBLES observations, and Ole Bolling Christensen for data processing help.
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