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

Abstract

Abstract. In recent years, there has been an increase in the number of climate studies addressing changes in extreme precipitation. A common step in these studies involves the assessment of the climate model performance. This is often measured by comparing climate model output with observational data. In the majority of such studies the characteristics and uncertainties of the observational data are neglected.This study addresses the influence of using different observational data sets to assess the climate model performance. Four different data sets covering Denmark using different gauge systems and comprising both networks of point measurements and gridded data sets are considered. Additionally, the influence of using different performance indices and metrics is addressed. A set of indices ranging from mean to extreme precipitation properties is calculated for all the data sets. For each of the observational data sets, the regional climate models (RCMs) are ranked according to their performance using two different metrics. These are based on the error in representing the indices and the spatial pattern.In comparison to the mean, extreme precipitation indices are highly dependent on the spatial resolution of the observations. The spatial pattern also shows differences between the observational data sets. These differences have a clear impact on the ranking of the climate models, which is highly dependent on the observational data set, the index and the metric used. The results highlight the need to be aware of the properties of observational data chosen in order to avoid overconfident and misleading conclusions with respect to climate model performance.

Regional Dynamical Downscaling and the CORDEX Initiative

“…Efforts have produced high-resolution data sets that can support the regional metrics of RCM performance (70,94), although one must recognize that the underlying observations may be more coarsely distributed. In addition, even when observations at suitably fine resolution are available, the RCM evaluation may be sensitive to the methods used to produce the observational data sets (95), such as quality-control measures and gridding techniques.…”

Local impact analysis of climate change on precipitation extremes: are high-resolution climate models needed for realistic simulations?

“…The precipitation extremes from the pixel E-OBS data follow the pattern of the point observations and the extremes are well represented in the pixel dataset. The smaller amounts from the gridded dataset are due to the fact that spatial averaging smooths out the extreme values (Hofstra et al, 2009;Sunyer et al, 2013).…”

Early warning of drought in Europe using the monthly ensemble system from ECMWF

“…Validation of the original data sets has been performed by Pereira et al (2013) and Sunyer et al (2013), who found that data sets from ECA & D show higher values for extreme precipitation, and E-OBS tends to over-smooth the data. This can generate some problems when analysing intense precipitation events but appears of secondary importance in drought analysis.…”