Harmonisation and trends of 20-years tropical tropospheric ozone data

Abstract

Abstract. Using the convective clouds differential (CCD) method on total ozone and cloud data from three European satellite instruments GOME/ ERS-2 (1995ERS-2 ( -2003, SCIAMACHY/Envisat (2002, and GOME-2/MetOp-A (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) it is possible to retrieve tropical tropospheric columns of ozone (TTCO) which are in good agreement with in-situ measurements. Finally, the datasets have been harmonised applying no correction to GOME data while GOME-2 has been corrected using for each grid-box the mean bias with respect SCIAMACHY for the years of common operation (2007)(2008)(2009)(2010)(2011)(2012). Depending on the choice of harmonisation, the magnitude, pattern, and uncertainty of the trend can strongly vary. The harmonisation represents an additional source of uncertainty in the merged dataset and derived trend estimates. For the preferred harmonised dataset,

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

“…The regions of the longest measurement periods required in the tropics for the |5| DU decade −1 trend detection (up to ∼ 16 years of IASI data) correspond to known patterns of widespread high O 3 : (a) above intense biomass burning in Amazonia and eastwards across the tropical Atlantic (Logan and Kirchhoff, 1986;Fishman et al, 1991;Moxim and Levy, 2000;Thompson and Wallace, 2000;Thompson et al, 2007;Sauvage et al, 2007), (b) eastwards of Africa across the southern Indian Ocean which is subject to large variations in the stratospheric influences during the winter-spring austral period (JJA-SON; Liu et al, 2016Liu et al, , 2017, (c) eastwards of Africa across the northern Indian Ocean to India likely due to large lightning NO x emissions above central Africa during the wet season associated with the northeastward jet conducting a so-called "O 3 river" (Tocquer et al, 2015) and (d) above regions of positive ENSO "chemical" effect in equatorial Asia/Australia and eastwards above northern and southern tropical regions (Wespes et al, 2016) explained by reduced rainfall and biomass fires during El Niño conditions (e.g., Worden et al, 2013). In fact, most of these patterns (a, b and d) are closely connected with strong El Niño events which extend the duration of the dry season and cause severe droughts, producing intense biomass-burning emissions, for instance, over South America (e.g., Chen et al, 2011;Lewis et al, 2011) and South Asia/Australia (e.g., Oman et al, 2013;Valks et al, 2014;Ziemke et al, 2015), and which alter the tropospheric circulation by increasing the transport of stratospheric O 3 into the troposphere (e.g., Voulgarakis et al, 2011b;Neu et al, 2014) and the transport of biomass-burning air masses to the Indian Ocean (Zhang et al, 2012). In summary, these large-scale indirect ENSOrelated variations in tropospheric O 3 and the lightning NO x impact on O 3 , which are not accounted for in the MLR by specific representative proxies, are misrepresented in the regression models.…”