Abstract

Abstract. Derivation of mean age of air (AoA) and age spectra from atmospheric measurements remains a challenge and often requires output from atmospheric models. This study tries to minimize the direct influence of model output 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 10 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 scales. 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 and 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 the extratropical and tropical tropopause peaked prior to both campaigns, but with lower mean AoA for WISE than PGS data. The ratio of moments for all retrieved age spectra for PGS and WISE is found to range between 0.52 and 2.81 years. We conclude 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.

Highlights

  • Spatial distributions of many greenhouse gases and ozonedepleting trace gases throughout the stratosphere are determined by the global mean meridional circulation, known as the Brewer–Dobson circulation (BDC), making it a crucial factor for the Earth’s radiative budget and climate (Shepherd, 2007; Solomon et al, 2010)

  • The extended inverse method is applied to observational data of short- and long-lived halogenated trace gases measured in the northern hemispheric (NH) lower stratosphere during the research campaigns PGS and Wave-driven Isentropic Exchange (WISE) of the High Altitude and Long Range (HALO) research aircraft

  • Model results indicate solidly that above 450 K the stratosphere is prevalently steered by entrainment across the tropical tropopause throughout the year

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Summary

Introduction

Spatial distributions of many greenhouse gases and ozonedepleting trace gases throughout the stratosphere are determined by the global mean meridional circulation, known as the Brewer–Dobson circulation (BDC), making it a crucial factor for the Earth’s radiative budget and climate (Shepherd, 2007; Solomon et al, 2010). The TTL is identified as the main entry point to the stratosphere, transport mechanisms across the extratropical tropopause play an important role in air composition in the lowermost stratosphere (LMS) below 380 K potential temperature (Olsen et al, 2004; Boothe and Homeyer, 2017). Those exchange processes exhibit their own distinct seasonality (Appenzeller et al, 1996; Schoeberl, 2004) and geographical distribution (Škerlak et al, 2014; Yang et al, 2016)

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