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Observations of historical global temperature are foundational for climate science and provide essential information for quantifying global and regional processes driving variability and change. However, historical temperature products are uncertain as they combine various sources of bias-corrected observations to estimate temperature everywhere on the Earth’s surface. The magnitude of this uncertainty is often negligible at the global mean-scale but is substantial for analyses in poorly sampled regions and/or earlier in the record. In this seminar, we will discuss how surface temperature datasets are created and how uncertainty is quantified. To place this uncertainty in context, we will discuss applications where accounting for this observational uncertainty is critical. Finally, we will present the NSF NCAR Climate Data Guide as a resource for navigating observational data in research applications.