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Influence of the Arctic Oscillation on the vertical distribution of clouds as observed by the A-Train constellation of satellites
SMHI, Research Department, Atmospheric remote sensing.ORCID iD: 0000-0002-6717-8343
SMHI, Research Department, Climate research - Rossby Centre.ORCID iD: 0000-0002-7815-4259
SMHI, Research Department, Air quality.ORCID iD: 0000-0002-5709-7507
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2012 (English)In: Atmospheric Chemistry And Physics, ISSN 1680-7316, E-ISSN 1680-7324, Vol. 12, no 21, 10535-10544 p.Article in journal (Refereed) Published
Abstract [en]

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 on-board 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 degrees 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.

Place, publisher, year, edition, pages
2012. Vol. 12, no 21, 10535-10544 p.
National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
URN: urn:nbn:se:smhi:diva-472DOI: 10.5194/acp-12-10535-2012ISI: 000310954400033OAI: diva2:805955
Available from: 2015-04-17 Created: 2015-04-14 Last updated: 2016-11-21Bibliographically approved

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