Abstract

In this paper, we assess how daily ozone (O₃) measurements from the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A platform can contribute to the analyses of the processes driving O₃ variability in the troposphere and the stratosphere and, in the future, to the monitoring of l... Show moreIn this paper, we assess how daily ozone (O₃) measurements from the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A platform can contribute to the analyses of the processes driving O₃ variability in the troposphere and the stratosphere and, in the future, to the monitoring of long-term trends. The temporal evolution of O₃ during the first 6 years of IASI (2008-2013) operation is investigated with multivariate regressions separately in four different layers (ground-300, 300-150, 150-25, 25-3 hPa), by adjusting to the daily time series averaged in 20° zonal bands, seasonal and linear trend terms along with important geophysical drivers of O₃ variation (e.g. solar flux, quasi-biennial oscillation (QBO)). The regression model is shown to perform generally very well with a strong dominance of the annual harmonic terms and significant contributions from O₃ drivers, in particular in the equatorial region where the QBO and the solar flux contribution dominate. More particularly, despite the short period of the IASI data set available up to now, two noticeable statistically significant apparent trends are inferred from the daily IASI measurements: a positive trend in the upper stratosphere (e.g. 1.74 ± 0.77 DU year⁻¹ between 30 and 50° S), which is consistent with other studies suggesting a turnaround for stratospheric O₃ recovery, and a negative trend in the troposphere at the mid-latitudes and high northern latitudes (e.g. -0.26 ± 0.11 DU year⁻¹ between 30 and 50° N), especially during summer and probably linked to the impact of decreasing ozone precursor emissions. The impact of the high temporal sampling of IASI on the uncertainty in the determination of O3 trend has been further explored by performing multivariate regressions on IASI monthly averages and on ground-based Fourier transform infrared (FTIR) measurements. Show less