Abstract

Abstract. Until now our understanding of the 11-year solar cycle signal (SCS) in stratospheric ozone has been largely based on high-quality but sparse ozone profiles from the Stratospheric Aerosol and Gas Experiment (SAGE) II or coarsely resolved ozone profiles from the nadir-viewing Solar Backscatter Ultraviolet Radiometer (SBUV) satellite instruments. Here, we analyse 16 years (2005–2020) of ozone profile measurements from the Microwave Limb Sounder (MLS) instrument on the Aura satellite to estimate the 11-year SCS in stratospheric ozone. Our analysis of Aura-MLS data suggests a single-peak-structured SCS profile (about 3 % near 4 hPa or 40 km) in tropical stratospheric ozone, which is significantly different to the SAGE II and SBUV-based double-peak-structured SCS. We also find that MLS-observed ozone variations are more consistent with ozone from our control model simulation that uses Naval Research Laboratory (NRL) v2 solar fluxes. However, in the lowermost stratosphere modelled ozone shows a negligible SCS compared to about 1 % in Aura-MLS data. An ensemble of ordinary least squares (OLS) and three regularised (lasso, ridge and elastic net) linear regression models confirms the robustness of the estimated SCS. In addition, our analysis of MLS and model simulations shows a large SCS in the Antarctic lower stratosphere that was not seen in earlier studies. We also analyse chemical transport model simulations with alternative solar flux data. We find that in the upper (and middle) stratosphere the model simulation with Solar Radiation and Climate Experiment (SORCE) satellite solar fluxes is also consistent with the MLS-derived SCS and agrees well with the control simulation and one which uses Spectral and Total Irradiance Reconstructions (SATIRE) solar fluxes. Hence, our model simulation suggests that with recent adjustments and corrections, SORCE data can be used to analyse effects of solar flux variations. Furthermore, analysis of a simulation with fixed solar fluxes and one with fixed (annually repeating) meteorology confirms that the implicit dynamical SCS in the (re)analysis data used to force the model is not enough to simulate the observed SCS in the middle and upper stratospheric ozone. Finally, we argue that the overall significantly different SCS compared to previous estimates might be due to a combination of different factors such as much denser MLS measurements, almost linear stratospheric chlorine loading changes over the analysis period, variations in the stratospheric dynamics as well as relatively unperturbed stratospheric aerosol layer that might have influenced earlier analyses.

Highlights

  • Changes in solar irradiance over the 11-year cycle are an important external forcing to the climate system

  • We find that in the upper stratosphere the model simulation with Solar Radiation and Climate Experiment (SORCE) satellite solar fluxes is consistent with the Microwave Limb Sounder (MLS)-derived solar cycle signal (SCS) and agrees well with the control simulation and one which uses Spectral and Total Irradiance Reconstructions (SATIRE) solar fluxes

  • Our key result is that we have presented an analysis of the solar cycle signal (SCS) in stratospheric ozone based on MLS v5 satellite data (2005–2020)

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Summary

Introduction

Changes in solar irradiance over the 11-year cycle are an important external forcing to the climate system. Chandra (1984) performed an initial attempt to estimate SCS using satellite-derived stratospheric ozone profiles from Nimbus-4 Backscatter UltraViolet (BUV) radiometer data for the 1970–1976 time period Their analysis suggested up to 12 % decrease in upper stratospheric ozone from solar maximum to solar minimum. Dhomse et al (2016) and Maycock et al (2016) analysed updated SAGE V7.0 ozone profiles to show a significantly reduced SCS in the upper stratosphere. Both of those studies noted that the SCS structure is altered significantly if the analysis is performed in mixing ratio units rather than native number density units.

Model setup and satellite data
Multivariate regression model
Results
Conclusions

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