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

Abstract

Abstract. In this work, we analyze the seasonal dependence of ozone trends in the stratosphere using four long-term merged data sets, 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 data sets 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 % per decade) and equinox (up to 3 % per decade) 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 % per decade to −2 % per decade) and in the lower-stratospheric spring (−2 % per decade to −4 % per decade), but positive (2 % per decade to 3 % per decade) 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 % per decade) and spring (up to −3 % per decade). In the lower mid-latitude stratosphere, our analysis points to a hemispheric asymmetry: during local summers and equinoxes, positive trends are observed in the south (+1 % per decade to +2 % per decade), while negative trends are observed in the north (−1 % per decade to −2 % per decade). 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.