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Evaluation of high-resolution simulations of daily-scale temperature and precipitation over the United States

TitleEvaluation of high-resolution simulations of daily-scale temperature and precipitation over the United States
Publication TypeManual Entry
Year of Publication2009
AuthorsWalker, Megan, and Noah Diffenbaugh
Climate Dynamics
PublisherSpringer Berlin / Heidelberg

Extreme climate events have been increasing over much of the world, and dynamical models predict further increases in response to enhanced greenhouse forcing. We examine the ability of a high-resolution nested climate model, RegCM3, to capture the statistics of daily-scale temperature and precipitation events over the conterminous United States, using observational and reanalysis data for comparison. Our analyses reveal that RegCM3 captures the pattern of mean, interannual variability, and trend in the tails of the daily temperature and precipitation distributions. However, consistent biases do exist, including wet biases in the topographically-complex regions of the western United States and hot biases in the southern and central United States. The biases in heavy precipitation in the western United States are associated with excessively strong surface and low-level winds. The biases in daily-scale temperature and precipitation in the southcentral United States are at least partially driven by biases in circulation and moisture fields. Further, the areas of agreement and disagreement with the observational data are not intuitive from analyzing the simulated mean seasonal temperature and precipitation fields alone. Our evaluation should enable more informed application and improvement of high-resolution climate models for the study of future changes in socially- and economically-relevant temperature and precipitation events.

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Community Notes

"We analyze the long-term annual statistics of 95th percentile daily maximum temperature (T95), 5th percentile daily minimum temperature (T05), and 95th percentile daily precipitation (P95) at each grid point"

"For each of these variables, we analyze the mean, interannual standard deviation, and linear trend across the years of the time series at each grid point. In order to calculate quantitative differences between fields from RegCM3 and NARR, RegCM3 data are regridded from their original grid to the NARR grid."


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