The oxygen isotopic composition of planktic foraminiferal calcite (
δ18Oc) is one of the most prevalent proxies used in the paleoceanographic community. The relationship between
δ18Oc, temperature, and seawater oxygen isotopic composition (
δ18Ow) is firmly rooted in thermodynamics, and experimental constraints are commonly used for sea surface temperature (SST) reconstructions. However, in marine sedimentary applications, additional sources of uncertainty emerge, and these uncertainty constraints have not as of yet been included in global calibration models. Here, we compile a global data set of over 2,600 marine sediment core top samples for five planktic species: Globigerinoides ruber, Trilobatus sacculifer, Globigerina bulloides, Neogloboquadrina incompta, and Neogloboquadrina pachyderma. We developed a suite of Bayesian regression models to calibrate the relationship between
δ18Oc and SST. Spanning SSTs from 0.0 to 29.5 °C, our annual model with species pooled together has a mean standard error of approximately 0.54‰. Accounting for seasonality and species‐specific differences improves model validation, reducing the mean standard error to 0.47‰. Example applications spanning the Late Quaternary show good agreement with independent alkenone‐based estimates. Our pooled calibration model may also be used for reconstruction in the deeper geological past, using modern planktic foraminifera as an analog for non‐extant species. Our core top‐based models provide a robust assessment of uncertainty in the
δ18Oc paleothermometer that can be used in statistical assessments of interproxy and model‐proxy comparisons. The suite of models is publicly available as the Open Source software library bayfox, for Python, R, and MATLAB/Octave.