1Swedish Meteorological and Hydrological Institute (SMHI), Norrköping, Sweden
2Department of Meteorology (MISU) and Bert Bolin Center for Climate Research, Stockholm University, Stockholm, Sweden
3Jet Propulsion Laboratory/CALTECH, NASA, Pasadena, USA
Abstract. 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 (AIRS-AQUA, CALIOP-CALIPSO and CPR-CloudSAT) covering two time periods (winter half years 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° N–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.