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Titel |
Tropical tropospheric ozone column retrieval for GOME-2 |
VerfasserIn |
P. Valks, N. Hao, S. Gimeno García, D. Loyola, M. Dameris, P. Jöckel, A. Delcloo |
Medientyp |
Artikel
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Sprache |
Englisch
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ISSN |
1867-1381
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Digitales Dokument |
URL |
Erschienen |
In: Atmospheric Measurement Techniques ; 7, no. 8 ; Nr. 7, no. 8 (2014-08-14), S.2513-2530 |
Datensatznummer |
250115871
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Publikation (Nr.) |
copernicus.org/amt-7-2513-2014.pdf |
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Zusammenfassung |
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. |
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