Seasonal stratospheric ozone trends over 2000–2018 derived from several merged data sets

Abstract. In this work, we analyse the seasonal dependence of ozone trends in the stratosphere using four long-term merged datasets: SAGE-CCI-OMPS, SAGE-OSIRIS-OMPS, GOZCARDS and SWOOSH which provide more than 30 years of monthly zonal mean ozone profiles in the stratosphere. We focus here on trends between 2000 and 2018. All datasets show similar results, although some discrepancies are observed. In the upper stratosphere, the trends are positive throughout all seasons and the majority of latitudes. The largest upper stratospheric ozone trends are observed during local winter (up to 6 % dec−1) and equinox (up to 3 % dec−1) at mid-latitudes. In the equatorial region, we find a very strong seasonal dependence of ozone trends at all altitudes: the trends vary from positive to negative, with the sign of transition depending on altitude and season. The trends are negative in the upper stratospheric winter (−1 to −2 % dec−1) and in the lower stratospheric spring (−2 to −4 % dec−1), but positive (2–3 % dec−1) at 30–35 km in spring, while the opposite pattern is observed in summer. The tropical trends below 25 km are negative and maximize during summer (up to −2 % dec−1) and spring (up to −3 % dec−1). In the lower mid-latitude stratosphere, our analysis indicates hemispheric asymmetry: during local summers and equinoxes, positive trends are observed in the South (+1 to +2 % dec−1) while negative trends are observed in the North (−1 to −2 % dec−1). We compare the seasonal dependence of ozone trends with available analyses of the seasonal dependence of stratospheric temperature trends. We find that ozone and temperature trends show positive correlation in the dynamically controlled lower stratosphere, and negative correlation above 30 km, where photochemistry dominates. Seasonal trend analysis gives information beyond that contained in annual mean trends, which can be helpful in order to better understand the role of dynamical variability in short-term trends and future ozone recovery predictions.