Termination of Solar Cycles and Correlated Tropospheric Variability

The Sun provides the energy required to sustain life on Earth and drive our planet's atmospheric circulation. However, establishing a solid physical connection between solar and tropospheric variability has posed a considerable challenge. The canon of solar variability is derived from the 400 years of observations that demonstrates the waxing and waning number of sunspots over an 11(-ish) year period. Recent research has demonstrated the significance of the underlying 22 years magnetic polarity cycle in establishing the shorter sunspot cycle. Integral to the manifestation of the latter is the spatiotemporal overlapping and migration of oppositely polarized magnetic bands. We demonstrate the impact of terminators-the end of Hale magnetic cycles-on the Sun's radiative output and particulate shielding of our atmosphere through the rapid global reconfiguration of solar magnetism. Using direct observation and proxies of solar activity going back some six decades we can, with high statistical significance, demonstrate a correlation between the occurrence of terminators and the largest swings of Earth's oceanic indices: the transition from El Nino to La Nina states of the central Pacific. This empirical relationship is a potential source of increased predictive skill for the understanding of El Nino climate variations, a high-stakes societal imperative given that El Nino impacts lives, property, and economic activity around the globe. A forecast of the Sun's global behavior places the next solar cycle termination in mid-2020; should a major oceanic swing follow, then the challenge becomes: when does correlation become causation and how does the process work?

Automated summary

The Sun's energy sustains life on Earth and influences atmospheric circulation; recent research has shown a connection between sunspot variability and magnetic polarity cycles, providing insights into climate change. By observing solar activity and oceanic indices, future climate variations can be predicted.