Multiscale Turbulence Effects on Solar Wind-Driven Auroral Activity Revealed by Interpretable Machine Learning

Auroral electrojet (AE) activity is a widely used system-level indicator of how the magnetosphere-ionosphere system responds to solar wind driving across a wide range of spatial and temporal scales. While large-scale solar wind parameters are known to control overall levels of geomagnetic activity, auroral responses often show substantial variability under similar upstream conditions. This suggests that additional aspects of solar wind variability, beyond mean magnetic field and plasma properties, may influence how energy and momentum are transferred into the coupled system. In particular, the role of multiscale solar wind turbulence and structured variability in modulating auroral activity remains incompletely understood.

In this work, we examine how multiscale solar wind turbulence contributes to auroral variability using an interpretable machine learning approach. OMNI observations are combined with AE index measurements to construct models that integrate conventional bulk drivers with measures describing solar wind variability across multiple timescales. Interpretable diagnostics are then used to assess how turbulence-related information influences auroral responses under different upstream conditions. While the overall improvement in forecasting skill obtained by including turbulence measures is modest, the results reveal consistent and scale-dependent contributions associated with structured solar wind variability. These findings suggest that solar wind turbulence plays a secondary but informative role in shaping auroral activity, providing insight into how mesoscale variability can modulate system-level coupling and highlighting the value of interpretable machine learning for advancing both physical understanding and space weather prediction.