Antarctica's Lambert–Amery system is often considered resilient to future climate changes owing to strong buttressing by the Amery Ice Shelf, yet emerging projections through 2300 suggest that sustained ocean warming could substantially alter its long-term mass balance. While recent probabilistic studies quantify present-day parametric uncertainty and propagate it to future sea-level contribution projections, they do not assess how rapidly these uncertainties will contract as forthcoming observations are assimilated. Here we quantify future learning rates for the Amery sector by building a sequential Bayesian calibration workflow that uses present-day (year 2015) as well as synthetic future observations to evaluate how quickly forthcoming data can tighten projections of sea-level contribution through 2300. Using simulations from the MPAS-Albany Land Ice (MALI) model augmented by Gaussian process emulators, we first generate 100 synthetic future observation trajectories of cumulative grounded mass change at 15-year intervals (2030–2300) under a high-greenhouse-gas-emission scenario, drawing from the present-day posterior distributions of six uncertain input parameters related to ice flow, calving, and ice-shelf melting. For each trajectory, we then sequentially recalibrate parameters at each analysis year using the present-day and all synthetic observations available up to that year, and propagate the recalibrated parameter uncertainties to generate updated projections of sea-level contribution. We quantify learning as the reduction in 90 % credible interval widths for both MALI parameters and sea-level contribution projections, characterizing variability across the 100 trajectories to assess uncertainty in the learning rate itself. Results reveal substantial but parameter-dependent learning, with the ice-shelf melt coefficient and basal slip exponent exhibiting the largest uncertainty reduction (≳8-fold by 2300). Learning about future sea-level contribution is time-horizon dependent: end-of-century (2100) projections show limited contraction (30 % reduction in <em>very-likely</em> ranges), whereas year-2200 and year-2300 projections exhibit rapid learning (∼6-fold reduction) after substantial ice-shelf thinning projected around 2150 creates stronger dynamic response which aids parameter learning. These findings indicate that near-term Amery contributions will remain difficult to tightly bound until substantial dynamical changes manifest (post-2150 in these simulations), but that sustained observations through that transition have high impact for reducing long-horizon risk. While our perfect-model assumption and simplified likelihood structure represent simplifications, the results provide guidance for assessing future learning of ice-sheet behavior.