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| Titel |
Technical Note: Temporal change in averaging kernels as a source of uncertainty in trend estimates of carbon monoxide retrieved from MOPITT |
| VerfasserIn |
J. Yoon, A. Pozzer, P. Hoor, D. Y. Chang, S. Beirle, T. Wagner, S. Schloegl, J. Lelieveld  , H. M. Worden |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 13, no. 22 ; Nr. 13, no. 22 (2013-11-21), S.11307-11316 |
| Datensatznummer |
250085828
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| Publikation (Nr.) |
 copernicus.org/acp-13-11307-2013.pdf |
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| Zusammenfassung |
| It has become possible to retrieve the global, long-term trends of trace
gases that are important to atmospheric chemistry, climate, and air quality
from satellite data records that span more than a decade. However, many of
the satellite remote sensing techniques produce measurements that have
variable sensitivity to the vertical profiles of atmospheric gases. In the
case of constrained retrievals like optimal estimation, this leads to a
varying amount of a priori information in the retrieval and is represented by
an averaging kernel (AK). In this study, we investigate
to what extent the estimation of trends from retrieved data
can be biased by temporal changes of averaging kernels used in the retrieval
algorithm. In particular, the surface carbon monoxide data retrieved from the
Measurements Of Pollution In The Troposphere (MOPITT) instrument from 2001 to
2010 were analyzed. As a practical example based on the MOPITT data, we show
that if the true atmospheric mixing ratio is continuously 50% higher or
lower than the a priori state, the temporal change of the averaging kernel at
the surface level gives rise to an artificial trend in retrieved surface
carbon monoxide, ranging from −10.71 to +13.21 ppbv yr−1 (−5.68
to +8.84 % yr−1) depending on location. Therefore, in the case of
surface (or near-surface level) CO derived from MOPITT, the AKs trends
multiplied by the difference between true and a priori states must be
quantified in order to estimate trend biases. |
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