An Integrated Uncertainty Framework for the China-MST 3.0 Global Surface Temperature Data Set

Global Mean Surface Temperature (GMST) is among the most important indicators of climate change, and its associated uncertainties affect the assessment of historical warming and the formulation of mitigation and adaptation policies. China-MST 3.0 is a newly updated global surface temperature data set that merges China-LSAT 2.1 for Land Surface Air Temperature (LSAT) and ERSST v6 for Sea Surface Temperature (SST). In this study, we develop a systematic and traceable uncertainty analysis framework for the construction process of this data set. Specifically, we comprehensively evaluate three components of LSAT uncertainty: observation, analysis, and coverage uncertainties, while describing SST uncertainty in terms of both parametric and reconstruction uncertainties. We also provide a quantitative assessment of the spatial and temporal evolution of these uncertainties. The results show that LSAT uncertainty is generally larger than that of SST and is mainly driven by coverage uncertainty. The overall uncertainty in GMST shows a significant downward trend, with the annual 1σ uncertainty falling below 0.03°C in recent decades, indicating high data reliability. However, uncertainty was high during the second-half of the 19th century and remains large at high latitudes in the Southern Hemisphere. Comparative analyses indicate that China-MST 3.0 is broadly consistent with other data sets in both the magnitude and temporal evolution of GMST uncertainty. These findings demonstrate the utility of China-MST 3.0 as a valuable tool for evaluating global warming since the 1850s.