A convolution of observational and model data to estimate age of air spectra in the northern hemispheric lower stratosphere

Abstract

Abstract. Derivation of mean age of air (AoA) and age spectra from atmospheric measurements remains a challenge and often requires data from atmospheric models. This study tries to minimize the direct influence of model data and presents an extension and application of a previously established inversion method to derive age spectra from mixing ratios of long- and short-lived trace gases. For a precise description of cross-tropopause transport processes, the inverse method is extended to incorporate air entrainment into the stratosphere across the tropical and extratropical tropopause. We first use simulations with the Chemical Lagrangian Model of the Stratosphere (CLaMS) to provide a general proof of concept of the extended principle in a controllable and consistent environment, where the method is applied to an idealized set of ten trace gases with predefined constant lifetimes and compared to reference model age spectra. In the second part of the study we apply the extended inverse method to atmospheric measurements of multiple long- and short-lived trace gases measured aboard the High Altitude and Long Range (HALO) research aircraft during the two research campaigns POLSTRACC/GW-LCYCLE/SALSA (PGS) and Wave-driven Isentropic Exchange (WISE). As some of the observed species undergo significant loss processes in the stratosphere, a Monte Carlo simulation is introduced to retrieve age spectra and chemical lifetimes in stepwise fashion and to account for the large uncertainties. Results show that in the idealized model scenario the inverse method retrieves age spectra robustly on annual and seasonal scale. The extension to multiple entry regions proves reasonable as our CLaMS simulations reveal that in the model between 50 % and 70 % of air in the lowermost stratosphere has entered through the extratropical tropopause (30°–90° N/S) on annual average. When applied to observational data of PGS and WISE the method derives age spectra and mean AoA with meaningful spatial distributions and quantitative range, yet large uncertainties. Results indicate that entrainment of fresh tropospheric air across both extratropical and tropical tropopause has peaked prior to both campaigns, but with lower mean AoA for WISE than PGS data. For a full assessment the ratio of moments for all retrieved age spectra is evaluated and found to range between 0.52 years and 2.81 years for PGS and WISE. It is concluded that the method derives reasonable and consistent age spectra using observations of chemically active trace gases. Our findings might contribute to an improved assessment of transport with age spectra in future studies.

Seasonal Variations of SF6, CO2, CH4, and N2O in the UT/LS Region due to Emissions, Transport, and Chemistry

“…The stratospheric mean age of air is defined as the average transit time for an air parcel to be transported from a reference source region in the troposphere to a specific location in the stratosphere. Several attempts have been made for calculating the age of air in the LS based on trace gas observations (Engel et al., 2017; Hauck et al., 2020; Sawa et al., 2015). In this study, we performed a comparative analysis of monthly mean age of air derived from CO 2 and SF 6 observations and model simulations during 2012–2013 and 2014–2015.…”

“…The analysis suggests that CO 2 could be a preferred choice as an age tracer in the older air regime of LS, and SF 6 in the younger air regime of LS. The estimation of age of air in the lower stratosphere from a chemical tracer with nonuniform source distribution on earth's surface is particularly challenging because of multiple entry‐points into the stratosphere (Hauck et al., 2020).…”

Deriving Tropospheric Transit Time Distributions Using Airborne Trace Gas Measurements: Uncertainty and Information Content

“…Although the stratospheric AoA has become a standard diagnostic using trace gas observations, such as SF 6 , CO 2 , N 2 O, CH 4 , and CFCs (e.g., Boering et al., 1996; Engel et al., 2009; Stiller et al., 2008; 2012), it is more difficult to derive an age spectrum using purely observations (e.g., Andrews et al., 1999; 2001; Ehhalt et al., 2007; Hauck et al., 2020). Similarly, few studies exist for deriving tropospheric TTD using observations (Holzer & Waugh, 2015; Luo et al., 2018).…”