ACP Atmospheric Chemistry and Physics ACP Atmos. Chem. Phys. 1680-7324 Copernicus Publications Göttingen, Germany 10.5194/acp-13-3619-2013 Assessment of the interannual variability and influence of the QBO and upwelling on tracer–tracer distributions of N<sub>2</sub>O and O<sub>3</sub> in the tropical lower stratosphere Khosrawi F. https://orcid.org/0000-0002-0261-7253 1 Müller R. https://orcid.org/0000-0002-5024-9977 2 Urban J. 3 Proffitt M. H. 4 Stiller G. https://orcid.org/0000-0003-2883-6873 5 Kiefer M. 5 Lossow S. https://orcid.org/0000-0003-2833-0522 5 Kinnison D. https://orcid.org/0000-0002-3418-0834 6 Olschewski F. 7 Riese M. https://orcid.org/0000-0001-6398-6493 2 Murtagh D. https://orcid.org/0000-0003-1539-3559 3 Department of Meteorology, Stockholm University, Stockholm, Sweden IEK-7: Stratosphere, Forschungszentrum Jülich, Jülich, Germany Department of Earth and Space Science, Chalmers University of Technology, Gothenburg, Sweden Proffitt Instruments, Austin, Texas, USA Karlsruhe Institute of Technology, Karlsruhe, Germany National Center for Atmospheric Research, Boulder, Colorado, USA Physics Department, Universität Wuppertal, Wuppertal, Germany 02 04 2013 13 7 3619 3641 Copyright: © 2013 F. Khosrawi et al. 2013 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://acp.copernicus.org/articles/13/3619/2013/acp-13-3619-2013.html The full text article is available as a PDF file from https://acp.copernicus.org/articles/13/3619/2013/acp-13-3619-2013.pdf

A modified form of tracer–tracer correlations of N<sub>2</sub>O and O<sub>3</sub> has been used as a tool for the evaluation of atmospheric photochemical models. Applying this method, monthly averages of N<sub>2</sub>O and O<sub>3</sub> are derived for both hemispheres by partitioning the data into altitude (or potential temperature) bins and then averaging over a fixed interval of N<sub>2</sub>O. In a previous study, the method has been successfully applied to the evaluation of two chemical transport models (CTMs) and one chemistry–climate model (CCM) using a 1 yr climatology derived from the Odin Sub-Millimetre Radiometer (Odin/SMR). However, the applicability of a 1 yr climatology of monthly averages of N<sub>2</sub>O and O<sub>3</sub> has been questioned due to the inability of some CCMs to simulate a specific year for the evaluation of CCMs. In this study, satellite measurements from Odin/SMR, the Aura Microwave Limb Sounder (Aura/MLS), the Michelson Interferometer for Passive Atmospheric Sounding on ENVISAT (ENVISAT/MIPAS), and the Cryogenic Infrared Spectrometers and Telescopes for the Atmosphere (CRISTA-1 and CRISTA-2) as well as model simulations from the Whole Atmosphere Community Climate Model (WACCM) are considered. By using seven to eight years of satellite measurements derived between 2003 and 2010 from Odin/SMR, Aura/MLS, ENVISAT/MIPAS and six years of model simulations from WACCM, the interannual variability of lower stratospheric monthly averages of N<sub>2</sub>O and O<sub>3</sub> is assessed. It is shown that the interannual variability of the monthly averages of N<sub>2</sub>O and O<sub>3</sub> is low, and thus can be easily distinguished from model deficiencies. Furthermore, it is investigated why large differences are found between Odin/SMR observations and model simulations from the Karlsruhe Simulation Model of the Middle Atmosphere (KASIMA) and the atmospheric general circulation model ECHAM5/Messy1 for the Northern and Southern Hemisphere tropics (0&deg; to 30&deg; N and 0&deg; to −30&deg; S, respectively). The differences between model simulations and observations are most likely caused by an underestimation of the quasi-biennial oscillation and tropical upwelling by the models as well as due to biases and/or instrument noise from the satellite instruments. A realistic consideration of the QBO in the model reduces the differences between model simulation and observations significantly. Finally, an intercomparison between Odin/SMR, Aura/MLS, ENVISAT/MIPAS and WACCM was performed. The comparison shows that these data sets are generally in good agreement, although some known biases of the data sets are clearly visible in the monthly averages. Nevertheless, the differences caused by the uncertainties of the satellite data sets are sufficiently small and can be clearly distinguished from model deficiencies. Thus, the method applied in this study is not only a valuable tool for model evaluation, but also for satellite data intercomparisons.

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