Disentangling Regional Drivers of Top Antarctic Snowfall Days With a Convolutional Neural Network

Abstract Snowfall is the primary contributor to Antarctic surface mass balance. Identifying regional‐scale mechanisms that drive heavy snowfall provides context for changes in Antarctic surface mass balance in a warmer climate. We compare drivers of top snowfall days across five Antarctic regions using machine learning and traditional synoptic diagnostics. A convolutional neural network identifies top snow days with an accuracy of 92%–94% per region when trained on just atmospheric moisture and low‐level meridional wind, highlighting the importance of atmospheric river‐like structures to top Antarctic snowfall days. The network's skill depends mainly on low‐level wind in East Antarctica and atmospheric moisture in West Antarctica, suggesting that dynamic processes are comparatively more important in driving East Antarctic top snowfall days. We leverage the quasi‐geostrophic omega equation to identify mechanisms for ascent and snowfall production, and we find that East Antarctic top snowfall days feature stronger synoptic‐scale forcing for ascent compared to West Antarctica.