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Abstract Global mean sea-level (GMSL) change can shed light on how the Earth system responds to warming. Glaciological evidence indicates that Earth’s ice sheets retreated inland of early industrial (1850 CE) extents during the Holocene (11.7-0 ka), yet previous work suggests that Holocene GMSL never surpassed early industrial levels. We merge sea-level data with a glacial isostatic adjustment model ensemble and reconstructions of postglacial thermosteric sea-level and mountain glacier evolution to estimate Holocene GMSL and ice volume. We show it is likely (probabilityP= 0.75) GMSL exceeded early industrial levels after 7.5ka, reaching 0.24 m (−3.3 to 1.0 m, 90% credible interval) above present by 3.2ka; Antarctica was likely (P = 0.78) smaller than present after 7ka; GMSL rise by 2150 will very likely (P = 0.9) be the fastest in the last 5000 years; and by 2060, GMSL will as likely than not (P = 0.5) be the highest in 115,000 years. 

Geological records of past environmental change provide crucial insights into long-term climate variability, trends, non-stationarity, and nonlinear feedback mechanisms. However, reconstructing spatiotemporal fields from these records is statistically challenging due to their sparse, indirect, and noisy nature. Here, we present PaleoSTeHM, a scalable and modern framework for spatiotemporal hierarchical modeling of paleo-environmental data. This framework enables the implementation of flexible statistical models that rigorously quantify spatial and temporal variability from geological data while clearly distinguishing measurement and inferential uncertainty from process variability. We illustrate its application by reconstructing temporal and spatiotemporal paleo-sea-level changes across multiple locations. Using various modeling and analysis choices, PaleoSTeHM demonstrates the impact of different methods on inference results and computational efficiency. Our results highlight the critical role of model selection in addressing specific paleo-environmental questions, showcasing the PaleoSTeHM framework's potential to enhance the robustness and transparency of paleo-environmental reconstructions.