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@ARTICLE{Casanueva:280221,
author = {Casanueva, A. and Kotlarski, S. and Herrera, S. and
Fernández, J. and Gutiérrez, J. M. and Boberg, F. and
Colette, A. and Christensen, O. B. and Görgen, Klaus and
Jacob, D. and Keuler, K. and Nikulin, G. and Teichmann, C.
and Vautard, R.},
title = {{D}aily precipitation statistics in a {EURO}-{CORDEX} {RCM}
ensemble: added value of raw and bias-corrected
high-resolution simulations},
journal = {Climate dynamics},
volume = {47},
number = {3},
issn = {0930-7575},
address = {Berlin},
publisher = {Springer},
reportid = {FZJ-2016-00029},
pages = {719-737},
year = {2016},
abstract = {Daily precipitation statistics as simulated by the
ERA-Interim-driven EURO-CORDEX regional climate model (RCM)
ensemble are evaluated over two distinct regions of the
European continent, namely the European Alps and Spain. The
potential added value of the high-resolution 12 km
experiments with respect to their 50 km resolution
counterparts is investigated. The statistics considered
consist of wet-day intensity and precipitation frequency as
a measure of mean precipitation, and three
precipitation-derived indicators (90th percentile on wet
days—90pWET, contribution of the very wet days to total
precipitation—R95pTOT and number of consecutive dry
days—CDD). As reference for model evaluation high
resolution gridded observational data over continental Spain
(Spain011/044) and the Alpine region (EURO4M-APGD) are used.
The assessment and comparison of the two resolutions is
accomplished not only on their original horizontal grids
(approximately 12 and 50 km), but the high-resolution RCMs
are additionally regridded onto the coarse 50 km grid by
grid cell aggregation for the direct comparison with the low
resolution simulations. The direct application of RCMs e.g.
in many impact modelling studies is hampered by model
biases. Therefore bias correction (BC) techniques are needed
at both resolutions to ensure a better agreement between
models and observations. In this work, the added value of
the high resolution (before and after the bias correction)
is assessed and the suitability of these BC methods is also
discussed. Three basic BC methods are applied to isolate the
effect of biases in mean precipitation, wet-day intensity
and wet-day frequency on the derived indicators. Daily
precipitation percentiles are strongly affected by biases in
the wet-day intensity, whereas the dry spells are better
represented when the simulated precipitation frequency is
adjusted to the observed one. This confirms that there is no
single optimal way to correct for RCM biases, since
correcting some distributional features typically leads to
an improvement of some aspects but to a deterioration of
others. Regarding mean seasonal biases before the BC, we
find only limited evidence for an added value of the higher
resolution in the precipitation intensity and frequency or
in the derived indicators. Thereby, evaluation results
considerably depend on the RCM, season and indicator
considered. High resolution simulations better reproduce the
indicators’ spatial patterns, especially in terms of
spatial correlation. However, this improvement is not
statistically significant after applying specific BC
methods.},
cin = {JSC / IBG-3},
ddc = {550},
cid = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)IBG-3-20101118},
pnm = {511 - Computational Science and Mathematical Methods
(POF3-511)},
pid = {G:(DE-HGF)POF3-511},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000382111300003},
doi = {10.1007/s00382-015-2865-x},
url = {https://juser.fz-juelich.de/record/280221},
}