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http://dx.doi.org/10.5140/JASS.2019.36.3.149

Association between Solar Variability and Teleconnection Index  

Kim, Jung-Hee (Department of Astronomy and Atmospheric Sciences, Kyungpook National University)
Chang, Heon-Young (Department of Astronomy and Atmospheric Sciences, Kyungpook National University)
Publication Information
Journal of Astronomy and Space Sciences / v.36, no.3, 2019 , pp. 149-157 More about this Journal
Abstract
In this study, we investigate the associations between the solar variability and teleconnection indices, which influence atmospheric circulation and subsequently, the spatial distribution of the global pressure system. A study of the link between the Sun and a large-scale mode of climate variability, which may indirectly affect the Earth's climate and weather, is crucial because the feedbacks of solar variability to an autogenic or internal process should be considered with due care. We have calculated the normalized cross-correlations of the total sunspot area, the total sunspot number, and the solar North-South asymmetry with teleconnection indices. We have found that the Southern Oscillation Index (SOI) index is anti-correlated with both solar activity and the solar North-South asymmetry, with a ~3-year lag. This finding not only agrees with the fact that El $Ni{\tilde{n}}o$ episodes are likely to occur around the solar maximum, but also explains why tropical cyclones occurring in the solar maximum periods and in El $Ni{\tilde{n}}o$ periods appear similar. Conversely, other teleconnection indices, such as the Arctic Oscillation (AO) index, the Antarctic Oscillation (AAO) index, and the Pacific-North American (PNA) index, are weakly or only slightly correlated with solar activity, which emphasizes that response of terrestrial climate and weather to solar variability are local in space. It is also found that correlations between teleconnection indices and solar activity are as good as correlations resulting from the teleconnection indices themselves.
Keywords
solar activity; terrestrial climate; data analysis;
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