Challenge of modelling GLORIA observations of UT/LMS trace gas and cloud distributions at high latitudes: a case study with state-of-the-art models

Water vapour and ozone are important for the thermal and radiative balance of the upper troposphere (UT) and lowermost stratosphere (LMS). Both species are modulated by transport processes. Chemical and microphysical processes affect them differently. Thus, representing the different processes and t...

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Bibliographic Details
Main Authors: Haenel, Florian, Woiwode, Wolfgang, Buchmüller, Jennifer, Friedl-Vallon, Felix, Höpfner, Michael, Johansson, Sören, Khosrawi, Farahnaz, Kirner, Oliver, Kleinert, Anne, Oelhaf, Hermann, Orphal, Johannes, Ruhnke, Roland, Sinnhuber, Björn-Martin, Ungermann, Jörn, Weimer, Michael, Braesicke, Peter
Format: Text
Language:English
Published: 2021
Subjects:
Online Access:https://doi.org/10.5194/acp-2021-574
https://acp.copernicus.org/preprints/acp-2021-574/
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Summary:Water vapour and ozone are important for the thermal and radiative balance of the upper troposphere (UT) and lowermost stratosphere (LMS). Both species are modulated by transport processes. Chemical and microphysical processes affect them differently. Thus, representing the different processes and their interactions is a challenging task for dynamical cores, chemical modules and microphysical parameterisations of state-of-the-art atmospheric model components. To test and improve the models, high resolution measurements of the UT/LMS are required. Here, we use measurements taken in a challenging case study by the GLORIA (Gimballed Limb Observer for Radiance Imaging of the Atmosphere) instrument on HALO. The German research aircraft HALO (High Altitude and LOng range research aircraft) performed a research flight on 26 February 2016, which covered deeply subsided air masses of the aged 2015/16 Arctic vortex, high-latitude LMS air masses, a highly textured troposphere-to-stratosphere exchange mixing region, and high-altitude cirrus clouds. Therefore, it provides a multifaceted case study for comparing GLORIA observations with state-of-the-art atmospheric model simulations in a complex UT/LMS region at a late stage of the Arctic winter 2015/16. Using GLORIA observations in this manifold scenario, we test the ability of the numerical weather prediction (NWP)-model ICON (ICOsahedral Nonhydrostatic) with the extension ART (Aerosols and Reactive Trace gases) and the chemistry-climate model (CCM) EMAC (ECHAM5/MESSy Atmospheric Chemistry) to model the UT/LMS composition of water vapour (H 2 O), ozone (O 3 ), nitric acid (HNO 3 ) and clouds. Within the scales resolved by the respective model, we find good overall agreement of both models with GLORIA. The applied high-resolution ICON-ART setup involving a R2B7 nest (local grid refinement with a horizontal resolution of about 20 km), covering the HALO flight region, reproduces mesoscale dynamical structures well. An observed troposphere-to-stratosphere exchange connected to an occluded Icelandic low is clearly reproduced by the model. Given the lower resolution (T106) of the nudged simulation of the EMAC model, we find that this model also reproduces these features well. Overall, trace gas mixing ratios simulated by both models are in a realistic range, and major cloud systems observed by GLORIA are mostly reproduced. However, we find both models to be affected by a well-known systematic moist-bias in the LMS. Further biases are diagnosed in the ICON-ART O 3 , EMAC H 2 O and EMAC HNO 3 distributions. Finally, we use sensitivity simulations to investigate (i) short-term cirrus cloud impacts on the H 2 O distribution (ICON-ART), (ii) the overall impact of polar winter chemistry and microphysical processing on O 3 and HNO 3 (ICON-ART/EMAC), (iii) the impact of the model resolution on simulated parameters (EMAC), and (iv) consequences of scavenging processes by cloud particles (EMAC). We find that changing of the horizontal model resolution results in notable systematic changes for all species in the LMS, while scavenging processes play only a role in case of HNO 3 . We need to understand the representativeness of our results. However, this is a unique opportunity to characterise model biases that potentially affect forecasts and projection (adversely), and to discover deficits and define paths for further model improvements.