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@ARTICLE{LavinGullon:878775,
author = {Lavin‐Gullon, Alvaro and Fernandez, Jesus and Bastin,
Sophie and Cardoso, Rita M. and Fita, Lluis and Giannaros,
Theodore M. and Görgen, Klaus and Gutierrez, Jose Manuel
and Kartsios, Stergios and Katragkou, Eleni and Lorenz,
Torge and Milovac, Josipa and Soares, Pedro M. M. and
Sobolowski, Stefan and Warrach‐Sagi, Kirsten},
title = {{I}nternal variability versus multi‐physics uncertainty
in a regional climate model},
journal = {International journal of climatology},
volume = {41},
number = {S1},
issn = {1097-0088},
address = {Chichester [u.a.]},
publisher = {Wiley},
reportid = {FZJ-2020-03037},
pages = {E656-E671},
year = {2021},
abstract = {In a recent study, Coppola et al. assessed the ability of
an ensemble of convection‐permitting models (CPM) to
simulate deep convection using three case studies. The
ensemble exhibited strong discrepancies between models,
which were attributed to various factors. In order to shed
some light on the issue, we quantify in this article the
uncertainty associated to different physical
parameterizations from that of using different initial
conditions, often referred to as the internal variability.
For this purpose, we establish a framework to quantify both
signals and we compare them for upper atmospheric
circulation and near‐surface variables. The analysis is
carried out in the context of the CORDEX Flagship Pilot
Study on Convective phenomena at high resolution over Europe
and the Mediterranean, in which the intermediate RCM WRF
simulations that serve to drive the CPM are run several
times with different parameterizations. For atmospheric
circulation (geopotential height), the sensitivity induced
by multi‐physics and the internal variability show
comparable magnitudes and a similar spatial distribution
pattern. For 2‐m temperature and 10‐m wind, the
simulations with different parameterizations show larger
differences than those launched with different initial
conditions. The systematic effect over 1 year shows
distinct patterns for the multi‐physics and the internal
variability. Therefore, the general lesson of this study is
that internal variability should be analysed in order to
properly distinguish the impact of other sources of
uncertainty, especially for short‐term sensitivity
simulations.},
cin = {IBG-3 / JARA-HPC},
ddc = {550},
cid = {I:(DE-Juel1)IBG-3-20101118 / $I:(DE-82)080012_20140620$},
pnm = {2173 - Agro-biogeosystems: controls, feedbacks and impact
(POF4-217) / Convection-permitting regional climate
modelling: Contribution to WCRP CORDEX Flagship Pilot Study
ensemble over Europe and joint analysis of water cycle
processes and properties $(jjsc39_20190501)$},
pid = {G:(DE-HGF)POF4-2173 / $G:(DE-Juel1)jjsc39_20190501$},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000550663400001},
doi = {10.1002/joc.6717},
url = {https://juser.fz-juelich.de/record/878775},
}