Trend and variability in ozone in the tropical lower stratosphere over 2.5 solar cycles observed by SAGE II and OSIRIS
- 1Institute of Space and Atmospheric Studies, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
- 2Environment Canada, Toronto, Ontario, Canada
- 3NASA Langley Research Center, Hampton, Virginia, USA
Abstract. We have extended the satellite-based ozone anomaly time series to the present (December 2012) by merging SAGE II (Stratospheric Aerosol and Gas Experiment II) with OSIRIS (Optical Spectrograph and Infrared Imager System) and correcting for the small bias (~0.5%) between them, determined using their temporal overlap of 4 years. Analysis of the merged data set (1984–2012) shows a statistically significant negative trend at all altitudes in the 18–25 km range, including a trend of (−4.6 ± 2.6)% decade−1 at 19.5 km where the relative standard error is a minimum. We are also able to replicate previously reported decadal trends in the tropical lower-stratospheric ozone anomaly based on SAGE II observations. Uncertainties are smaller on the merged trend than the SAGE II trend at all altitudes. Underlying strong fluctuations in ozone anomaly due to El Niño–Southern Oscillation (ENSO), the altitude-dependent quasi-biennial oscillation, and tropopause pressure need to be taken into account to reduce trend uncertainties and, in the case of ENSO, to accurately determine the linear trend just above the tropopause. We also compare the observed ozone trend with a calculated trend that uses information on tropical upwelling and its temporal trend from model simulations, tropopause pressure trend information derived from reanalysis data, and vertical profiles from SAGE II and OSIRIS to determine the vertical gradient of ozone and its trend. We show that the observed trend agrees with the calculated trend and that the magnitude of the calculated trend is dominated by increased tropical upwelling, with minor but increasing contribution from the vertical ozone gradient trend as the tropical tropopause is approached. Improvements are suggested for future regression modelling efforts which could reduce trend uncertainties and biases in trend magnitudes, thereby allowing accurate trend detection to extend below 18 km.