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| Titel |
Influence of the Arctic Oscillation on the vertical distribution of clouds as observed by the A-Train constellation of satellites |
| VerfasserIn |
A. Devasthale, M. Tjernström, M. Caian, M. A. Thomas, B. H. Kahn, E. J. Fetzer |
| 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 ; 12, no. 21 ; Nr. 12, no. 21 (2012-11-12), S.10535-10544 |
| Datensatznummer |
250011581
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| Publikation (Nr.) |
 copernicus.org/acp-12-10535-2012.pdf |
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| Zusammenfassung |
The main purpose of this study is to investigate the influence of the Arctic
Oscillation (AO), the dominant mode of natural variability over the
northerly high latitudes, on the spatial (horizontal and vertical)
distribution of clouds in the Arctic. To that end, we use a suite of sensors
onboard NASA's A-Train satellites that provide accurate observations of the
distribution of clouds along with information on atmospheric thermodynamics.
Data from three independent sensors are used (AQUA-AIRS, CALIOP-CALIPSO and
CPR-CloudSat) covering two time periods (winter half years, November through
March, of 2002–2011 and 2006–2011, respectively) along with data from the
ERA-Interim reanalysis.
We show that the zonal vertical distribution of cloud fraction anomalies
averaged over 67–82° N
to a first approximation follows a dipole structure
(referred to as "Greenland cloud dipole anomaly", GCDA), such that during
the positive phase of the AO, positive and negative cloud anomalies are
observed eastwards and westward of Greenland respectively, while the
opposite is true for the negative phase of AO. By investigating the
concurrent meteorological conditions (temperature, humidity and winds), we
show that differences in the meridional energy and moisture transport during
the positive and negative phases of the AO and the associated thermodynamics
are responsible for the conditions that are conducive for the formation of
this dipole structure. All three satellite sensors broadly observe this
large-scale GCDA despite differences in their sensitivities, spatio-temporal
and vertical resolutions, and the available lengths of data records,
indicating the robustness of the results. The present study also provides a
compelling case to carry out process-based evaluation of global and regional
climate models. |
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