In this study, we provide a worldwide overview on the expected sensitivity of downscaling studies to reanalysis choice. To this aim, we assess the similarity of middle-tropospheric variables -which are important for the development of both dynamical and statistical downscaling schemes- from ERA-40 and NCEP/NCAR reanalysis data on daily timescale. For estimating distributional similarity, we use two comparable scores: the two-sample Kolmogorov-Smirnov statistic and the PDF-score. In addition, the similarity of the day-to-day sequences is evaluated with the Pearson Correlation Coefficient.
As most important results, the PDF-score is found to be inappropriate if the underlying data follows 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 geopotential and temperature, significant distributional dissimilarities for specific humidity are found in almost any 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 -as opposite to statistical approaches,- their results are expected to be more sensitive to reanalysis choice.
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