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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]

Uncertainty in climate projections is driven by three components: scenario uncertainty, intermodel uncertainty, and internal variability. Although socioeconomic climate impact studies increasingly take into account the first two components, little attention has been paid to the role of internal variability, although underestimating this uncertainty may lead to underestimating the socioeconomic costs of climate change. Using large ensembles fromseven coupled general circulation models with a total of 414 model runs, we partition the climate uncertainty in classic dose–response models relating countylevel corn yield, mortality, and per-capita gross domestic product to temperature in the continental United States. The partitioning of uncertainty depends on the time frame of projection, the impact model, and the geographic region. Internal variability represents more than 50% of the total climate uncertainty in certain projections, including mortality projections for the early 21st century, although its relative influence decreases over time. We recommend including uncertainty due to internal variability for many projections of temperature-driven impacts, including early-century and midcentury projections, projections in regions with high internal variability such as the Upper Midwest United States, and impacts driven by nonlinear relationships. [ABSTRACT FROM AUTHOR]

We outline a new and improved uncertainty analysis for the Goddard Institute for Space Studies Surface Temperature product version 4 (GISTEMP v4). Historical spatial variations in surface temperature anomalies are derived from historical weather station data and ocean data from ships, buoys, and other sensors. Uncertainties arise from measurement uncertainty, changes in spatial coverage of the station record, and systematic biases due to technology shifts and land cover changes. Previously published uncertainty estimates for GISTEMP included only the effect of incomplete station coverage. Here, we update this term using currently available spatial distributions of source data, state‐of‐the‐art reanalyses, and incorporate independently derived estimates for ocean data processing, station homogenization, and other structural biases. The resulting 95% uncertainties are near 0.05 °C in the global annual mean for the last 50 years and increase going back further in time reaching 0.15 °C in 1880. In addition, we quantify the benefits and inherent uncertainty due to the GISTEMP interpolation and averaging method. We use the total uncertainties to estimate the probability for each record year in the GISTEMP to actually be the true record year (to that date) and conclude with 86% likelihood that 2016 was indeed the hottest year of the instrumental period (so far). Key Points: A total uncertainty analysis for GISTEMP is presented for the first timeUncertainty in global mean surface temperature is roughly 0.05 degrees Celsius in recent decades increasing to 0.15 degrees Celsius in the nineteenth centuryAnnual mean uncertainties are small relative to the long‐term trend [ABSTRACT FROM AUTHOR]