Abstract Uncertainties in estimates of Equilibrium Climate Sensitivity (ECS) and Transient Climate Response (TCR) are influenced by observational temperature data sets. Variability exists not just among the data products, but also within the creation of each one. This includes significant variations among ensemble members within a single data product. Using the optimal fingerprint approach combined with Bayesian updating, we quantify the uncertainties in ECS and TCR estimates arising from both individual data sets and their various groupings. Our methodology, utilizing both spatial and temporal data, shows impacts on the estimates of ECS and TCR. As we assess different groupings of observational data products, we observe that using products sharing identical Sea Surface Temperatures (SST) introduce discernible biases. These results highlight that variations among ensemble members within a single data product are as influential as the disparities across multiple data products.