On the importance of observational data properties when assessing regional climate model performance of extreme precipitation

摘要

In recent years, there has been an increase in the number of climate studiesaddressing changes in extreme precipitation. A common step in these studiesinvolves the assessment of the climate model performance. This is oftenmeasured by comparing climate model output with observational data. In themajority of such studies the characteristics and uncertainties of theobservational data are neglected.This study addresses the influence of using different observational data setsto assess the climate model performance. Four different data sets coveringDenmark using different gauge systems and comprising both networks of pointmeasurements and gridded data sets are considered. Additionally, theinfluence of using different performance indices and metrics is addressed. Aset of indices ranging from mean to extreme precipitation properties iscalculated for all the data sets. For each of the observational data sets, theregional climate models (RCMs) are ranked according to their performanceusing two different metrics. These are based on the error in representingthe indices and the spatial pattern.In comparison to the mean, extreme precipitation indices are highlydependent on the spatial resolution of the observations. The spatial patternalso shows differences between the observational data sets. These differenceshave a clear impact on the ranking of the climate models, which is highlydependent on the observational data set, the index and the metric used. Theresults highlight the need to be aware of the properties of observationaldata chosen in order to avoid overconfident and misleading conclusions withrespect to climate model performance.