Modeling frontal ablation in global glacier models (Joint Bayesian)

Accurate estimation of frontal ablation of water‑terminating glaciers is essential for assessing global glacier mass change and projecting sea‑level rise. We present a hybrid framework that couples a SERMeQ‑based frontal‑ablation component with climatic mass‑balance from PyGEM and ice dynamics from OGGM, and we introduce an adaptive particle‑batch smoother to jointly calibrate all model parameters simultaneously. The model simulates centreline length change and mass‑balance components at monthly resolution and updates flow‑line geometry accordingly. Calibration assimilates both decadal averaged geodetic mass‑balance estimates and remote‑sensing annual timeseries terminus‑position changes, constraining the coupled dynamics and ablation processes within a single, physically consistent framework. Applied regionally to 71 tidewater glaciers in Svalbard, the framework reproduces observed seasonal behaviour and hindcasts, while providing improved projections of future glacier evolution. These results offer more robust regional estimates of contributions to sea‑level rise and freshwater availability and identify priorities for further reducing uncertainties in frontal‑ablation estimates.