Model studies of short‐term variations induced in trace gases by particle precipitation in the mesosphere and lower thermosphere. Issue 10 (25th October 2016) - Fytterer, T.; Bender, S.; Berger, U.; Nieder, H.; Sinnhuber, M.; Wissing, J. M.
Abstract: The 3‐D global chemistry and transport model (3dCTM) was used to investigate NO, OH, and O3 from January 2002 to May 2010 between 60 km and 133 km. Their daytime and nighttime mean zonal means (55°–75° geomagnetic latitude) were analyzed with respect to short‐term variations associated with particle precipitation. The corresponding ionization rates were derived from the 3‐D atmospheric ionization module Osnabrück (AIMOS), which is based on particle flux measurements. The trace gas variations with respect to their background were investigated by using a superposed epoch analysis. The 27 day signature associated with particle precipitation is found in NO, while it is only indicated in OH and O3 during winter. A varying solar spectrum associated with the 11 year solar cycle causes modifications of this signal up to 10%, while the main patterns are conserved. Published observations show a clear 27 day signal, qualitatively agreeing with the model results at altitudes >70 km except for O3 in Northern Hemisphere winter. Further differences occur with respect to the magnitude of the trace gas variations, primarily attributed to the different trace gas background and dynamical variations of the background atmosphere. Absolute OH variations are overestimated by the 3dCTM during winter, while the opposite is true for O3 . These differences might originate from an unknown offset in AIMOS, incorrect chemical reaction rates, a different background of H2 O and O3, and the model dynamics. However, their nonlinear relationship and their altitude of largest response are qualitatively captured in Southern Hemisphere winter. Key Points: The 27 day solar rotational signal in atmospheric trace gas simulations induced by particle precipitation Strong impact on trace gas response due to dynamical variability Overestimation of particle impact but qualitatively captured chemistry involved in the model