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Observational uncertainty and regional climate model evaluation: A pan-European perspective

Journal: International Journal of Climatology
Year: 2017  
Status: In print
In this status since: 25 Jul 2017
PDF file: paper_VALUEWG2_obsuncertainty_specialissueINTJCLIM_revised01_proofs.pdf
Link to PDF: PDF-Online
DOI: 10.1002/joc.5249
Authors:
Kotlarski, S., Szabó, P., , Räty, O, Keuler, K., Soares, P., Cardoso, R.M., Bosshard, T., Pagé, C, Boberg, F., , Isotta, F., Jaczewski, A., Kreienkamp, F, Liniger, M.A., Lussana, C.

The influence of uncertainties in gridded observational reference data on regional climate model (RCM) evaluation is quantified on a pan-European scale. Three different reference datasets are considered: the coarse-resolved E-OBS dataset, a compilation of regional high-resolution gridded products (HR) and the European-scale MESAN reanalysis. Five high-resolution ERA-Interim driven RCM experiments of the EURO-CORDEX initiative are evaluated against each of these references over eight European sub-regions and considering a range of performance metrics for mean daily temperature and daily precipitation. The spatial scale of the evaluation is 0.22°, i.e. the grid spacing of the coarsest dataset in the exercise (E-OBS).

While the three reference grids agree on the overall mean climatology, differences can be pronounced over individual regions. These differences partly translate into RCM evaluation uncertainty. Still, for most cases observational uncertainty is smaller than RCM uncertainty. For individual sub-regions and performance metrics, however, observational uncertainty can dominate. This is especially true for precipitation and for metrics targeting the wet-day frequency, the pattern correlation and the distributional similarity. In some cases also the spatially averaged mean bias can be considerably affected.

An illustrative ranking exercise highlights the overall effect of observational uncertainty on RCM ranking. Over individual sub-domains, the choice of a specific reference can modify RCM ranks by up to four levels (out of five RCMs). For most cases, however, RCM ranks are stable irrespective of the reference. These results provide a two-fold picture: model uncertainty dominates for most regions and for most performance metrics considered, and observational uncertainty plays a minor role. For individual cases, however, observational uncertainty can be pronounced and needs to be definitely taken into account. Results can to some extent also depend on the treatment of potential precipitation undercatch in the observational reference.

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