Oscillatory Coupling between Solar–Terrestrial Dynamics and El Niño–Southern Oscillation (ENSO) during Equinox Periods: An Astroclimatological Decadal Analysis

The El Niño–Southern Oscillation (ENSO) is a major driver of global interannual climate variability. This study investigates the potential influence of solar–terrestrial oscillations—particularly anomalies in solar declination and Earth–Sun distance—during equinox periods from 2013 to 2024. Using satellite-derived net shortwave radiation as an integrative indicator reflecting both atmospheric conditions and astronomical influences, together with precise astronomical ephemerides and polar motion data, a multiple linear regression model captures approximately 70% of the variability in the Oceanic Niño Index (ONI). The model identifies solar declination as the principal astronomical driver, with Earth–Sun distance contributing significantly, and polar motion showing limited influence at the decadal scale, though potentially relevant over longer periods.These findings support an astroclimatological framework in which subtle orbital and axial variations act as persistent and quantifiable modulators of seasonal ENSO phases. Rather than serving as a static backdrop, astronomical factors generate unique annual configurations due to complex orbital and rotational dynamics, resulting in variable solar forcing conditions that affect ocean–atmosphere coupling. Anomalies in net shortwave radiation reflect the dynamic interplay between celestial geometry and geophysical processes, mediating ENSO variability.While this study primarily aimed to analyze the influence of key astronomical variables, it also yielded a model with potential for ENSO prediction. This highlights the value of incorporating astronomical metrics into ENSO analysis, especially during equinoctial transitions. The findings suggest ENSO variability is embedded within a coupled system shaped by persistent astronomical oscillations, reflecting a planetary-scale link between celestial mechanics and climate variability.