Deriving stratospheric age of air spectra using chemically active trace gases

Marius Hauck, Frauke Fritsch, Hella Garny, Andreas Engel
2018 Atmospheric Chemistry and Physics Discussions  
<p><strong>Abstract.</strong> Analysis of stratospheric transport from an observational point of view is frequently realized by evaluation of mean age of air values from long-lived trace gases. However, this provides more insight into general transport strength and less into its mechanism. Deriving complete transit time distributions (age spectra) is desirable, but their deduction from direct measurements is difficult and so far primarily achieved by assumptions about dynamics and spectra
more » ... s and spectra themselves. This paper introduces a modified version of an inverse method to infer age spectra from mixing ratios of short-lived trace gases. For a full description of transport seasonality the formulation includes an imposed seasonal cycle to gain multimodal spectra. The EMAC model simulation used for a proof of concept features an idealized dataset of 40 radioactive trace gases with different chemical lifetimes as well as 40 chemically inert pulsed trace gases to calculate pulse age spectra. Annual and seasonal mean inverse spectra are compared to pulse spectra including first and second moments as well as the ratio between them to assess the performance on these time scales. Results indicate that the modified inverse age spectra match the annual and seasonal pulse age spectra well on global scale beyond 1.5 years mean age of air. The imposed seasonal cycle emerges as a reliable tool to include transport seasonality in the age spectra. Below 1.5 years mean age of air, tropospheric influence intensifies and breaks the assumption of single entry through the tropical tropopause, leading to inaccurate spectra in particular in the northern hemisphere. The imposed seasonal cycle wrongly prescribes seasonal entry in this lower region and does not lead to a better agreement between inverse and pulse age spectra without further improvement. As the inverse method aims for future implementation on in situ observational data, possible critical factors for this purpose are delineated finally.</p>
doi:10.5194/acp-2018-991 fatcat:qkrrl7b4wbdqrdshykjxcxlehe