Improvements in the GISTEMP Uncertainty Model

TLDR

Uncertainties in GISTEMP arise from measurement error, changing station coverage, and systematic biases from technology and land‑cover shifts, with earlier estimates accounting only for incomplete coverage. The study aims to develop an improved uncertainty analysis for GISTEMP v4 and assess the impact of its interpolation and averaging methods. The authors updated uncertainty estimates by incorporating spatial distributions of station and ocean data, reanalyses, and independent bias corrections, and evaluated the interpolation and averaging effects. The revised uncertainties are about 0.05 °C for recent decades, rising to 0.15 °C in 1880, and the analysis yields an 87 % probability that 2016 was the hottest instrumental year.

Abstract

Abstract 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 87% likelihood that 2016 was indeed the hottest year of the instrumental period (so far).