Sensitivity of Attribution of Anthropogenic Near-Surface Warming to Observational Uncertainty

Abstract

The impact of including comprehensive estimates of observational uncertainties on a detection and attribution analysis of twentieth-century near-surface temperature variations is investigated. The error model of HadCRUT4, a dataset of land near-surface air temperatures and sea surface temperatures, provides estimates of measurement, sampling, and bias adjustment uncertainties. These uncertainties are incorporated into an optimal detection analysis that regresses simulated large-scale temporal and spatial variations in near-surface temperatures, driven by well-mixed greenhouse gas variations and other anthropogenic and natural factors, against observed changes. The inclusion of bias adjustment uncertainties increases the variance of the regression scaling factors and the range of attributed warming from well-mixed greenhouse gases by less than 20%. Including estimates of measurement and sampling errors has a much smaller impact on the results. The range of attributable greenhouse gas warming is larger across analyses exploring dataset structural uncertainty. The impact of observational uncertainties on the detection analysis is found to be small compared to other sources of uncertainty, such as model variability and methodological choices, but it cannot be ruled out that on different spatial and temporal scales this source of uncertainty may be more important. The results support previous conclusions that there is a dominant anthropogenic greenhouse gas influence on twentieth-century near-surface temperature increases.