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The historical global temperature record is an essential data product for quantifying the variability and change of the Earth system. In recent years, better characterization of observational uncertainty in global and hemispheric trends has become available, but the methodologies are not necessarily applicable to analyses at smaller regional areas, or monthly or seasonal means, where station sparsity and other systematic issues contribute to greater uncertainty. This study presents a gridded uncertainty ensemble of historical surface temperature anomalies from the Goddard Institute for Space Studies (GISS) Surface Temperature (GISTEMP) product. This ensemble characterizes the complex spatial and temporal correlation structure of uncertainty, enabling better uncertainty propagation for climate and applied science in applications of historical temperature products at spatial scales from global to regional and temporal scales from centennial to monthly. This work details the methodology for generating the uncertainty ensemble, presents key statistics of the uncertainty evolution over space and time, and provides best practices for using the uncertainty ensemble in future studies. Summary statistics from the uncertainty ensemble agree well with the previous GISTEMP global uncertainty assessment, providing confidence in both. Plain Language Summary: NASA's historical surface temperature product, GISTEMP, combines measurements of sea surface temperature and land surface air temperature to estimate how temperature has changed on Earth since 1880. In this study, we combine all known sources of uncertainty in these measurements to determine the best estimate as well as the likely interval for surface temperature in the global average as well as on a latitude‐longitude grid. Results agree with previous uncertainty assessments of GISTEMP as well as uncertainty assessments of other major global temperature products. The uncertainty is quantified through an uncertainty ensemble, or 200 equally likely reconstructions of monthly mean surface temperature. This uncertainty ensemble makes it easy to incorporate the uncertainty in surface temperature in subsequent studies. Key Points: A 200‐member ensemble has been created to quantify uncertainty in the GISTEMP history of surface temperature anomaliesThis ensemble enables more accurate statistical analyses of key global change metricsThe median ensemble estimate agrees well with the operational GISTEMP analysis and other global products [ABSTRACT FROM AUTHOR]

This analysis estimates uncertainty in the NOAA global surface temperature (GST) version 5 (NOAAGlobalTemp v5) product, which consists of sea surface temperature (SST) from the Extended Reconstructed SST version 5 (ERSSTv5) and land surface air temperature (LSAT) from the Global Historical Climatology Network monthly version 4 (GHCNm v4). Total uncertainty in SST and LSAT consists of parametric and reconstruction uncertainties. The parametric uncertainty represents the dependence of SST/LSAT reconstructions on selecting 28 (6) internal parameters of SST (LSAT), and is estimated by a 1000-member ensemble from 1854 to 2016. The reconstruction uncertainty represents the residual error of using a limited number of 140 (65) modes for SST (LSAT). Uncertainty is quantified at the global scale as well as the local grid scale. Uncertainties in SST and LSAT at the local grid scale are larger in the earlier period (1880s–1910s) and during the two world wars due to sparse observations, then decrease in the modern period (1950s–2010s) due to increased data coverage. Uncertainties in SST and LSAT at the global scale are much smaller than those at the local grid scale due to error cancellations by averaging. Uncertainties are smaller in SST than in LSAT due to smaller SST variabilities. Comparisons show that GST and its uncertainty in NOAAGlobalTemp v5 are comparable to those in other internationally recognized GST products. The differences between NOAAGlobalTemp v5 and other GST products are within their uncertainties at the 95% confidence level. [ABSTRACT FROM AUTHOR]