The Southern Annular Mode (SAM) is an extratropical pattern that influences the climate of all Southern Hemisphere. However, the variability of this mode is an active area of research. The influence of lower frequency modes on SAM is a path to better knowledge about this pattern. The relationship between Quasi-Biennial Oscillation (QBO) and SAM’s counterpart in the Northern Hemisphere (Northern Annular Mode) has been addressed by previous work. Still, few studies focus on the association between QBO and SAM. The goal of this work was to evaluate the possible QBO-SAM relationship through statistical analyses. This association was investigated by comparing QBO and SAM indices, the latter on different levels of the troposphere and stratosphere, for the 1981-2010 period. The wavelet analysis showed that the SAM indices for troposphere and stratosphere presented variability in many scales, including a two-year band. Cross-wavelets techniques between QBO and SAM ratified that this relation has a complex interaction. There was a significant common high power around the two-year band, with lags varying over the analyzed period, including no lag. Further analysis without lag confirmed previous studies, indicating that the negative (positive) SAM phase is more frequent for easterly (westerly) QBO. However, this was not valid for all months. Some additional analysis suggested that the upward wave propagation to the stratosphere for each QBO phase changes the stratospheric jet and, consequently, the SAM phase.

Abstract. The broad geographical coverage and high temporal and spatial resolution of geostationary satellite data provide an excellent opportunity to collect information on variables whose spatial distribution and temporal variability are not adequately represented by the in situ networks. This study focuses on assessing the effectiveness of two geostationary satellite-based sunshine duration (SDU) datasets over Brazil, given the relevance of SDU to various fields, such as agriculture and energy sectors, to ensure reliable SDU data over the country. The analyzed datasets are the operational products provided by the Satellite Application Facility on Climate Monitoring (CMSAF), that uses data achieved with the Meteorological Satellite (Meteosat) series, and by the Satellite and Meteorological Sensors Divison of the National Institute for Space Research (DISSM/INPE), that employs Geostationary Operational Environmental Satellite (GOES) data. The analyzed period ranges from September 2013 to December 2017. The mean bias error (MBE), mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (r) and scatterplots between satellite products and in situ daily SDU measurements provided by the National Institute of Meteorology (INMET) were used to access the products performance. They were calculated on a monthly basis and grouped into climate regions. The statistical parameters exhibited a uniform spatial distribution, indicating homogeneity within a given region. Except for the Tropical Northeast Oriental (TNO) region, there were no significant seasonal dependencies observed. The Mean Bias Error (MBE) values for both satellite products were generally low across most regions in Brazil, mainly between 0 and 1 hour. The correlation coefficient (r) results indicated a strong agreement between the estimated values and the observed data, with an overall r value exceeding 0.8. Nevertheless, there were notable discrepancies in specific areas. The CMSAF product showed a tendency to overestimate observations in the TNO region, with MBE consistently exceeding 1 hour for all months, while the DISSM product exhibited a negative gradient of MBE values in the west-east direction, in the northern portion of Brazil. The scatterplots for the TNO region revealed that the underestimation pattern observed in the DISSM product was influenced by the sky condition, with more accurate estimations observed under cloudy skies. Additional analysis suggested that the biases observed might be attributed to the misrepresentation of clear-sky reflectance. In the case of the CMSAF product, the overestimation tendency observed in the TNO region appeared to be a result of systematic underestimation of the Effective Cloud Albedo. The findings indicated that both satellite-based SDU products generally exhibited good agreement with the ground observations across Brazil, although their performance varied across different regions and seasons. The analyzed operational satellite products present a reliable source of data to several applications, being an asset due to its high spatial resolution and low time latency.