Technical note: Lowermost-stratosphere moist bias in ECMWF IFS model diagnosed from airborne GLORIA observations during winter–spring 2016

• Main Authors: Woiwode, Wolfgang, Dörnbrack, Andreas, Polichtchouk, Inna, Johansson, Sören, Harvey, Ben, Höpfner, Michael, Ungermann, Jörn, Friedl-Vallon, Felix
• Format: Article in Journal/Newspaper
• Language: English
• Published: European Geosciences Union 2020
• Subjects: Numerical weather prediction; ECMWF; Stratosphere; GLORIA; Limb-imaging; FTIR; ddc:550; Earth sciences; info:eu-repo/classification/ddc/550; Greenland
• Online Access: https://publikationen.bibliothek.kit.edu/1000127584; https://publikationen.bibliothek.kit.edu/1000127584/95987281; https://doi.org/10.5445/IR/1000127584

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.