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The influence of synoptic airflow on UK daily precipitation extremes. Part II: regional climate model and E-OBS data validation

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Abstract

We investigate how well the variability of extreme daily precipitation events across the United Kingdom is represented in a set of regional climate models and the E-OBS gridded data set. Instead of simply evaluating the climatologies of extreme precipitation measures, we develop an approach to validate the representation of physical mechanisms controlling extreme precipitation variability. In part I of this study we applied a statistical model to investigate the influence of the synoptic scale atmospheric circulation on extreme precipitation using observational rain gauge data. More specifically, airflow strength, direction and vorticity are used as predictors for the parameters of the generalised extreme value (GEV) distribution of local precipitation extremes. Here we employ this statistical model for our validation study. In a first step, the statistical model is calibrated against a gridded precipitation data set provided by the UK Met Office. In a second step, the same statistical model is calibrated against 14 ERA40 driven 25 km resolution RCMs from the ENSEMBLES project and the E-OBS gridded data set. Validation indices describing relevant physical mechanisms are derived from the statistical models for observations and RCMs and are compared using pattern standard deviation, pattern correlation and centered pattern root mean squared error as validation measures. The results for the different RCMs and E-OBS are visualised using Taylor diagrams. We show that the RCMs adequately simulate moderately extreme precipitation and the influence of airflow strength and vorticity on precipitation extremes, but show deficits in representing the influence of airflow direction. Also very rare extremes are misrepresented, but this result is afflicted with a high uncertainty. E-OBS shows considerable biases, in particular in regions of sparse data. The proposed approach might be used to validate other physical relationships in regional as well as global climate models.

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Notes

  1. With respect to splines with zero mean for the strength and vorticity dependence; as the splines calculated by VGAM have a mean different from zero, we actually approximated the offsets by time averages.

  2. Note that Fig. 3d in Maraun et al. (2011) shows the same measure for the rain gauge data, and not a measure based on the 90 and 10% quantiles as stated in that manuscript. The difference in the patterns, however, is marginal.

  3. Note again that Fig. 3e, f in Maraun et al. (2011) show the same measures, and not measures based on the 90 and 10% quantiles as stated in that manuscript. The difference in the patterns is again marginal. Additionally, we have confused the two panels; panel (e) shows the relationship for anti-cyclonic airflow, panel (f) for cyclonic airflow.

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Acknowledgments

Douglas Maraun started the research for this manuscript at the Climatic Research Unit, receiving funding from the NERC Flood Risk From Extreme Events (FREE) programme (NE/E002412/1). We would like to thank Peter Stott and the UK Met Office for granting us permission to use the 5 km gridded daily precipitation data set. The software is written in R (R Development Core Team 2006) and based on the VGAM package by Thomas Yee and the EVD package by Alec Stephenson. The re-gridding of the UKMO 5km data set was done with the R package fields by Doug Nychka and colleagues.

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Authors and Affiliations

  1. Leibniz Institute of Marine Sciences (IFM-GEOMAR), Düsternbrooker Weg 20, 24105, Kiel, Germany

    Douglas Maraun

  2. Climatic Research Unit, School of Environmental Sciences, Norwich, NR4 7TJ, UK

    Timothy J. Osborn

  3. Freie Universität Berlin, Institut für Meteorologie, Carl-Heinrich-Becker-Weg 6-10, 12165, Berlin, Germany

    Henning W. Rust

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  1. Douglas Maraun
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Correspondence to Douglas Maraun.

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Maraun, D., Osborn, T.J. & Rust, H.W. The influence of synoptic airflow on UK daily precipitation extremes. Part II: regional climate model and E-OBS data validation. Clim Dyn 39, 287–301 (2012). https://doi.org/10.1007/s00382-011-1176-0

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