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@ARTICLE{Woiwode:901998,
author = {Woiwode, Wolfgang and Dörnbrack, Andreas and Polichtchouk,
Inna and Johansson, Sören and Harvey, Ben and Höpfner,
Michael and Ungermann, Jörn and Friedl-Vallon, Felix},
title = {{T}echnical note: {L}owermost-stratosphere moist bias in
{ECMWF} {IFS} model diagnosed from airborne {GLORIA}
observations during winter–spring 2016},
journal = {Atmospheric chemistry and physics},
volume = {20},
number = {23},
issn = {1680-7324},
address = {Katlenburg-Lindau},
publisher = {EGU},
reportid = {FZJ-2021-03964},
pages = {15379 - 15387},
year = {2020},
abstract = {Numerical weather forecast systems like the ECMWF IFS
(European Centre for Medium-Range Weather Forecasts –
Integrated Forecasting System) are known to be affected by a
moist bias in the extratropical lowermost stratosphere (LMS)
which results in a systematic cold bias there. We use
high-spatial-resolution water vapor measurements by the
airborne infrared limb-imager GLORIA (Gimballed Limb
Observer for Radiance Imaging of the Atmosphere) during the
PGS (POLSTRACC/GW-LCYCLE-II/SALSA) campaign to study the LMS
moist bias in ECMWF analyses and 12 h forecasts from
January to March 2016. Thereby, we exploit the
two-dimensional observational capabilities of GLORIA, when
compared to in situ observations, and the higher vertical
and horizontal resolution, when compared to satellite
observations. Using GLORIA observations taken during five
flights in the polar sub-vortex region around Scandinavia
and Greenland, we diagnose a systematic moist bias in the
LMS exceeding $+50 \%$ (January) to $+30 \%$ (March) at
potential vorticity levels from 10 PVU (∼ highest
level accessed with suitable coverage) to 7 PVU. In the
diagnosed time period, the moist bias decreases at the
highest and driest air masses observed but clearly persists
at lower levels until mid-March. Sensitivity experiments
with more frequent temporal output, and lower or higher
horizontal and vertical resolution, show the short-term
forecasts to be practically insensitive to these parameters
on timescales of < 12 h. Our results confirm that the
diagnosed moist bias is already present in the initial
conditions (i.e., the analysis) and thus support the
hypothesis that the cold bias develops as a result of
forecast initialization. The moist bias in the analysis
might be explained by a model bias together with the lack of
water vapor observations suitable for assimilation above the
tropopause.},
cin = {IEK-7},
ddc = {550},
cid = {I:(DE-Juel1)IEK-7-20101013},
pnm = {2112 - Climate Feedbacks (POF4-211)},
pid = {G:(DE-HGF)POF4-2112},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000599523900003},
doi = {10.5194/acp-20-15379-2020},
url = {https://juser.fz-juelich.de/record/901998},
}