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Abstract Glacier speedups occur on daily to centennial timescales. While basal water and subglacial drainage configuration are thought to drive glacier speedups across these timescales, it remains unclear whether this forcing always occurs through the same mechanisms. Here, we explore whether the enthalpy model of glacier surging can explain speedups over a broader range of timescales if modified to account for seasonality in surface melt and continuous water supply to the glacier bed. We simulate velocity oscillations that range from seasonal to years. Our model results more closely resemble observations of surges than previous model versions because ice flow variability at seasonal and multi‐year timescales is reproduced simultaneously through hydrological forcing. Under favorable conditions, seasonal water delivery to the bed gradually accumulates in a poorly‐connected basal drainage system, priming the glacier to surge. Surges themselves are marked by high water fluxes and enthalpy drainage from the glacier base. 

Self-supervised learning(SSL) is essential to obtain foundation models in NLP and CV domains via effectively leveraging knowledge in large-scale unlabeled data. The reason for its success is that a suitable SSL design can help the model to follow the neural scaling law, i.e., the performance consistently improves with increasing model and dataset sizes. However, it remains a mystery whether existing SSL in the graph domain can follow the scaling behavior toward building Graph Foundation Models~(GFMs) with large-scale pre-training. In this study, we examine whether existing graph SSL techniques can follow the neural scaling behavior with the potential to serve as the essential component for GFMs. Our benchmark includes comprehensive SSL technique implementations with analysis conducted on both the conventional SSL setting and many new settings adopted in other domains. Surprisingly, despite the SSL loss continuously decreasing, no existing graph SSL techniques follow the neural scaling behavior on the downstream performance. The model performance only merely fluctuates on different data scales and model scales. Instead of the scales, the key factors influencing the performance are the choices of model architecture and pretext task design. This paper examines existing SSL techniques for the feasibility of Graph SSL techniques in developing GFMs and opens a new direction for graph SSL design with the new evaluation prototype. Our code implementation is available online to ease reproducibility https://github.com/HaitaoMao/GraphSSLScaling.