Articles | Volume 7, issue 8
https://doi.org/10.5194/amt-7-2513-2014
https://doi.org/10.5194/amt-7-2513-2014
Research article
 | 
14 Aug 2014
Research article |  | 14 Aug 2014

Tropical tropospheric ozone column retrieval for GOME-2

P. Valks, N. Hao, S. Gimeno Garcia, D. Loyola, M. Dameris, P. Jöckel, and A. Delcloo

Abstract. This paper presents the operational retrieval of tropical tropospheric ozone columns (TOCs) from the Second Global Ozone Monitoring Experiment (GOME-2) instruments using the convective-cloud-differential (CCD) method. The retrieval is based on total ozone and cloud property data provided by the GOME Data Processor (GDP) 4.7, and uses above-cloud and clear-sky ozone column measurements to derive a monthly mean TOC between 20° N and 20° S. Validation of the GOME-2 TOC with several tropical ozonesonde sites shows good agreement, with a high correlation between the GOME-2 and sonde measurements, and small biases within ~ 3 DU. The TOC data have been used in combination with tropospheric NO2 measurements from GOME-2 to analyse the effect of the 2009–2010 El Niño–Southern Oscillation (ENSO) on the tropospheric ozone distribution in the tropics. El Niño induced dry conditions in September–October 2009 resulted in relatively high tropospheric ozone columns over the southern Indian Ocean and northern Australia, while La Niña conditions in September–October 2010 resulted in a strong increase in tropospheric NO2 in South America, and enhanced ozone in the eastern Pacific and South America. Comparisons of the GOME-2 tropospheric ozone data with simulations of the ECHAM/MESSy Atmospheric Chemistry (EMAC) model for 2009 El Niño conditions illustrate the usefulness of the GOME-2 TOC measurements in evaluating chemistry climate models (CCMs). Evaluation of CCMs with appropriate satellite observations helps to identify strengths and weaknesses of the model systems, providing a better understanding of driving mechanisms and adequate relations and feedbacks in the Earth atmosphere, and finally leading to improved models.