On the Use of Reanalysis Data for Downscaling

Abstract

Abstract In this study, a worldwide overview on the expected sensitivity of downscaling studies to reanalysis choice is provided. To this end, the similarity of middle-tropospheric variables—which are important for the development of both dynamical and statistical downscaling schemes—from 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and NCEP–NCAR reanalysis data on a daily time scale is assessed. For estimating the distributional similarity, two comparable scores are used: the two-sample Kolmogorov–Smirnov statistic and the probability density function (PDF) score. In addition, the similarity of the day-to-day sequences is evaluated with the Pearson correlation coefficient. As the most important results demonstrated, the PDF score is found to be inappropriate if the underlying data follow a mixed distribution. By providing global similarity maps for each variable under study, regions where reanalysis data should not assumed to be “perfect” are detected. In contrast to the geopotential and temperature, significant distributional dissimilarities for specific humidity are found in almost every region of the world. Moreover, for the latter these differences not only occur in the mean, but also in higher-order moments. However, when considering standardized anomalies, distributional and serial dissimilarities are negligible over most extratropical land areas. Since transformed reanalysis data are not appropriate for regional climate models—in opposition to statistical approaches—their results are expected to be more sensitive to reanalysis choice.