Decrease in tropospheric O<sub>3</sub> levels in the Northern Hemisphere observed by IASI

Abstract

Abstract. In this study, we describe the recent changes in the tropospheric ozone (O3) columns measured by the Infrared Atmospheric Sounding Interferometer (IASI), onboard the Metop satellite, during the first 9 years of operation (January 2008 to May 2017). Using appropriate multivariate regression methods, we differentiate significant linear trends from other sources of O3 variations captured by IASI. The geographical patterns of the adjusted O3 trends are provided and discussed on the global scale. Given the large contribution of the natural variability in comparison with that of the trend (25–85 % vs. 15–50 %, respectively) to the total O3 variations, we estimate that additional years of IASI measurements are generally required to detect the estimated O3 trends with high precision. Globally, additional 6 months to 6 years of measurements, depending on the regions and the seasons, are needed to detect a trend of |5| DU decade−1. An exception is interestingly found during summer at mid- and high latitudes of the Northern Hemisphere (NH; ∼ 40 to ∼ 75∘ N), where the large absolute fitted trend values (∼ |0.5| DU yr−1 on average) combined with the small model residuals (∼ 10 %) allow for detection of a band-like pattern of significant negative trends. Despite no consensus in terms of tropospheric O3 trends having been reached from the available independent datasets (UV or IR satellites, O3 sondes, aircrafts, ground-based measurements, etc.) for the reasons that are discussed in the text, this finding is consistent with the reported decrease in O3 precursor emissions in recent years, especially in Europe and USA. The influence of continental pollution on that latitudinal band is further investigated and supported by the analysis of the O3–CO relationship (in terms of correlation coefficient, regression slope and covariance) that we found to be the strongest at northern midlatitudes in summer.

A tropopause-based a priori for IASI-SOFRID Ozone retrievals: improvements and validation

“…in-situ profiles in order to perform a fully informative validation. This is of particular importance when the satellite data are 30 used for issues such as the ozone seasonal to interannual variabilities (Wespes et al, 2017;Peiro et al, 2018) or to document the long term tropospheric ozone tendencies (Gaudel et al, 2018;Wespes et al, 2018;Dufour et al, 2018). Indeed, the application of Equation 1 implies the mixing of information between the different layers.…”

“…Therefore, the variabilities and the drifts computed from raw and smoothed sonde data may be different and need to be documented. Raw ozone sonde data have been compared to IASI retrievals in few studies at the beginning of the IASI period (Barret et al, 2011;Dufour et al, 2012) but have been disregarded in more recent validation work (Boynard et al, 2016(Boynard et al, , 2018. The importance of raw data validation regarding 5 seasonal and interannual variabilities and trends analyses will be highlighted in details in section 4.…”

“…This study aimed at assessing the quality of two different versions of SOFRID-O3 at the global scale and over the 10 year IASI period using ozonesonde from the WOUDC. SOFRID-O3 v1.6 retrievals are based on a single a priori profile like most other global IASI O 3 retrievals (Barret et al, 2011;Dufour et al, 2012;Boynard et al, 2016Boynard et al, , 2018. In V3.5 the a priori is dynamically selected from an O 3 profile climatology (Sofieva et al, 2014) based on latitude, season and the tropopause height.…”

“…We have divided the atmosphere in 5 layers which are described in Table 1. The Troposphere 2 layer has been selected for comparison with Boynard et al (2018Boynard et al ( , 2016 who did not compute a tropopause based TOC for their validation (see section 5). The DFS corresponding to these different layers is displayed in Figure 1 for V1.6 and V3.5 averaged over the validation dataset.…”

“…iii) the correlation coefficient (R) that document the consistency and phase of the variabilities of both datasets and (iv) the ratio of the standard deviations of both datasets which documents the goodness of the amplitude of the retrieval variability. In the case of IASI O 3 , the first two indicators are frequently computed(Boynard et al, 2016(Boynard et al, , 2018 Barret et al, 2011) but the last ones are rarely compared (Dufour et al, 2012) which makes most validation exercices incomplete.15…”