Articles | Volume 16, issue 15
https://doi.org/10.5194/acp-16-10021-2016
https://doi.org/10.5194/acp-16-10021-2016
Research article
 | 
09 Aug 2016
Research article |  | 09 Aug 2016

The representation of solar cycle signals in stratospheric ozone – Part 1: A comparison of recently updated satellite observations

Amanda C. Maycock, Katja Matthes, Susann Tegtmeier, Rémi Thiéblemont, and Lon Hood

Abstract. Changes in incoming solar ultraviolet radiation over the 11-year solar cycle affect stratospheric ozone abundances. It is important to quantify the magnitude, structure, and seasonality of the associated solar-ozone response (SOR) to understand the impact of the 11-year solar cycle on climate. Part 1 of this two-part study uses multiple linear regression analysis to extract the SOR in a number of recently updated satellite ozone datasets covering different periods within the epoch 1970 to 2013. The annual mean SOR in the updated version 7.0 (v7.0) Stratospheric Aerosol and Gas Experiment (SAGE) II number density dataset (1984–2004) is very consistent with that found in the previous v6.2. In contrast, we find a substantial decrease in the magnitude of the SOR in the tropical upper stratosphere in the SAGE II v7.0 mixing ratio dataset (∼ 1 %) compared to the v6.2 (∼ 4 %). This difference is shown to be largely attributable to the change in the independent stratospheric temperature dataset used to convert SAGE II ozone number densities to mixing ratios. Since these temperature records contain substantial uncertainties, we suggest that datasets based on SAGE II number densities are currently most reliable for evaluating the SOR. We further analyse three extended ozone datasets that combine SAGE II v7.0 number densities with more recent GOMOS (Global Ozone Monitoring by Occultation of Stars) or OSIRIS (Optical Spectrograph and Infrared Imager System) measurements. The extended SAGE–OSIRIS dataset (1984–2013) shows a smaller and less statistically significant SOR across much of the tropical upper stratosphere compared to the SAGE II data alone. In contrast, the two SAGE–GOMOS datasets (1984–2011) show SORs that are in closer agreement with the original SAGE II data and therefore appear to provide a more reliable estimate of the SOR. We also analyse the SOR in the recent Solar Backscatter Ultraviolet Instrument (SBUV) Merged Ozone Dataset (SBUVMOD) version 8.6 (VN8.6) (1970–2012) and SBUV Merged Cohesive VN8.6 (1978–2012) datasets and compare them to the previous SBUVMOD VN8.0 (1970–2009). Over their full lengths, the three records generally agree in terms of the broad magnitude and structure of the annual mean SOR. The main difference is that SBUVMOD VN8.6 shows a smaller and less significant SOR in the tropical upper stratosphere and therefore more closely resembles the SAGE II v7.0 mixing ratio data than does the SBUV Merged Cohesive VN8.6, which has a more continuous SOR of ∼ 2 % in this region. The sparse spatial and temporal sampling of limb satellite instruments prohibits the extraction of sub-annual variations in the SOR from SAGE-based datasets. However, the SBUVMOD VN8.6 dataset suggests substantial month-to-month variations in the SOR, particularly in the winter extratropics, which may be important for the proposed high-latitude dynamical response to the solar cycle. Overall, the results highlight substantial uncertainties in the magnitude and structure of the observed SOR from different satellite records. The implications of these uncertainties for understanding and modelling the effects of solar variability on climate should be explored.

Short summary
The impact of changes in incoming solar radiation on stratospheric ozone has important impacts on the atmosphere. Understanding this ozone response is crucial for constraining how solar activity affects climate. This study analyses the solar ozone response (SOR) in satellite datasets and shows that there are substantial differences in the magnitude and spatial structure across different records. In particular, the SOR in the new SAGE v7.0 mixing ratio data is smaller than in the previous v6.2.
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