Bayesian Calibration for the Arctic Sea Ice Biomarker IP₂₅

Abstract Sea ice plays multiple important roles in regulating the global climate. Rapid sea ice loss in the Arctic has been documented over recent decades, yet our understanding of long‐term sea ice variability and its feedbacks remains limited by a lack of quantitative sea ice reconstructions. The sea ice diatom‐derived biomarker has been combined with sterols produced by open‐water phytoplankton in the index as a sea ice proxy to achieve semi‐quantitative reconstructions. Here, we analyze a compilation of over 600 published core‐top measurements of paired with brassicasterol and/or dinosterol across (sub‐)Arctic oceans to calculate a newln() index that correlates nonlinearly with sea ice concentration. Leveraging sediment trap and sea ice observational studies, we develop a spatially varying Bayesian calibration ( BaySIC ) for ln() to account for its non‐stationary relationship with sea ice concentration and other environmental drivers (e.g., sea surface salinity). The model is fully invertible, allowing probabilistic forward modeling of the ln() index as well as inverse modeling of past sea ice concentration with bi‐directional uncertainty quantification. BaySIC facilitates direct proxy‐model comparisons and palaeoclimate data assimilation, providing the polar proxy constraints currently missing in climate model simulations and enabling, for the first time, fully quantitative Arctic sea ice reconstructions.